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Causal research: definition, examples and how to use it.

16 min read Causal research enables market researchers to predict hypothetical occurrences & outcomes while improving existing strategies. Discover how this research can decrease employee retention & increase customer success for your business.

What is causal research?

Causal research, also known as explanatory research or causal-comparative research, identifies the extent and nature of cause-and-effect relationships between two or more variables.

It’s often used by companies to determine the impact of changes in products, features, or services process on critical company metrics. Some examples:

  • How does rebranding of a product influence intent to purchase?
  • How would expansion to a new market segment affect projected sales?
  • What would be the impact of a price increase or decrease on customer loyalty?

To maintain the accuracy of causal research, ‘confounding variables’ or influences — e.g. those that could distort the results — are controlled. This is done either by keeping them constant in the creation of data, or by using statistical methods. These variables are identified before the start of the research experiment.

As well as the above, research teams will outline several other variables and principles in causal research:

  • Independent variables

The variables that may cause direct changes in another variable. For example, the effect of truancy on a student’s grade point average. The independent variable is therefore class attendance.

  • Control variables

These are the components that remain unchanged during the experiment so researchers can better understand what conditions create a cause-and-effect relationship.  

This describes the cause-and-effect relationship. When researchers find causation (or the cause), they’ve conducted all the processes necessary to prove it exists.

  • Correlation

Any relationship between two variables in the experiment. It’s important to note that correlation doesn’t automatically mean causation. Researchers will typically establish correlation before proving cause-and-effect.

  • Experimental design

Researchers use experimental design to define the parameters of the experiment — e.g. categorizing participants into different groups.

  • Dependent variables

These are measurable variables that may change or are influenced by the independent variable. For example, in an experiment about whether or not terrain influences running speed, your dependent variable is the terrain.  

Why is causal research useful?

It’s useful because it enables market researchers to predict hypothetical occurrences and outcomes while improving existing strategies. This allows businesses to create plans that benefit the company. It’s also a great research method because researchers can immediately see how variables affect each other and under what circumstances.

Also, once the first experiment has been completed, researchers can use the learnings from the analysis to repeat the experiment or apply the findings to other scenarios. Because of this, it’s widely used to help understand the impact of changes in internal or commercial strategy to the business bottom line.

Some examples include:

  • Understanding how overall training levels are improved by introducing new courses
  • Examining which variations in wording make potential customers more interested in buying a product
  • Testing a market’s response to a brand-new line of products and/or services

So, how does causal research compare and differ from other research types?

Well, there are a few research types that are used to find answers to some of the examples above:

1. Exploratory research

As its name suggests, exploratory research involves assessing a situation (or situations) where the problem isn’t clear. Through this approach, researchers can test different avenues and ideas to establish facts and gain a better understanding.

Researchers can also use it to first navigate a topic and identify which variables are important. Because no area is off-limits, the research is flexible and adapts to the investigations as it progresses.

Finally, this approach is unstructured and often involves gathering qualitative data, giving the researcher freedom to progress the research according to their thoughts and assessment. However, this may make results susceptible to researcher bias and may limit the extent to which a topic is explored.

2. Descriptive research

Descriptive research is all about describing the characteristics of the population, phenomenon or scenario studied. It focuses more on the “what” of the research subject than the “why”.

For example, a clothing brand wants to understand the fashion purchasing trends amongst buyers in California — so they conduct a demographic survey of the region, gather population data and then run descriptive research. The study will help them to uncover purchasing patterns amongst fashion buyers in California, but not necessarily why those patterns exist.

As the research happens in a natural setting, variables can cross-contaminate other variables, making it harder to isolate cause and effect relationships. Therefore, further research will be required if more causal information is needed.

Get started on your market research journey with CoreXM

How is causal research different from the other two methods above?

Well, causal research looks at what variables are involved in a problem and ‘why’ they act a certain way. As the experiment takes place in a controlled setting (thanks to controlled variables) it’s easier to identify cause-and-effect amongst variables.

Furthermore, researchers can carry out causal research at any stage in the process, though it’s usually carried out in the later stages once more is known about a particular topic or situation.

Finally, compared to the other two methods, causal research is more structured, and researchers can combine it with exploratory and descriptive research to assist with research goals.

Summary of three research types

causal research table

What are the advantages of causal research?

  • Improve experiences

By understanding which variables have positive impacts on target variables (like sales revenue or customer loyalty), businesses can improve their processes, return on investment, and the experiences they offer customers and employees.

  • Help companies improve internally

By conducting causal research, management can make informed decisions about improving their employee experience and internal operations. For example, understanding which variables led to an increase in staff turnover.

  • Repeat experiments to enhance reliability and accuracy of results

When variables are identified, researchers can replicate cause-and-effect with ease, providing them with reliable data and results to draw insights from.

  • Test out new theories or ideas

If causal research is able to pinpoint the exact outcome of mixing together different variables, research teams have the ability to test out ideas in the same way to create viable proof of concepts.

  • Fix issues quickly

Once an undesirable effect’s cause is identified, researchers and management can take action to reduce the impact of it or remove it entirely, resulting in better outcomes.

What are the disadvantages of causal research?

  • Provides information to competitors

If you plan to publish your research, it provides information about your plans to your competitors. For example, they might use your research outcomes to identify what you are up to and enter the market before you.

  • Difficult to administer

Causal research is often difficult to administer because it’s not possible to control the effects of extraneous variables.

  • Time and money constraints

Budgetary and time constraints can make this type of research expensive to conduct and repeat. Also, if an initial attempt doesn’t provide a cause and effect relationship, the ROI is wasted and could impact the appetite for future repeat experiments.

  • Requires additional research to ensure validity

You can’t rely on just the outcomes of causal research as it’s inaccurate. It’s best to conduct other types of research alongside it to confirm its output.

  • Trouble establishing cause and effect

Researchers might identify that two variables are connected, but struggle to determine which is the cause and which variable is the effect.

  • Risk of contamination

There’s always the risk that people outside your market or area of study could affect the results of your research. For example, if you’re conducting a retail store study, shoppers outside your ‘test parameters’ shop at your store and skew the results.

How can you use causal research effectively?

To better highlight how you can use causal research across functions or markets, here are a few examples:

Market and advertising research

A company might want to know if their new advertising campaign or marketing campaign is having a positive impact. So, their research team can carry out a causal research project to see which variables cause a positive or negative effect on the campaign.

For example, a cold-weather apparel company in a winter ski-resort town may see an increase in sales generated after a targeted campaign to skiers. To see if one caused the other, the research team could set up a duplicate experiment to see if the same campaign would generate sales from non-skiers. If the results reduce or change, then it’s likely that the campaign had a direct effect on skiers to encourage them to purchase products.

Improving customer experiences and loyalty levels

Customers enjoy shopping with brands that align with their own values, and they’re more likely to buy and present the brand positively to other potential shoppers as a result. So, it’s in your best interest to deliver great experiences and retain your customers.

For example, the Harvard Business Review found that an increase in customer retention rates by 5% increased profits by 25% to 95%. But let’s say you want to increase your own, how can you identify which variables contribute to it?Using causal research, you can test hypotheses about which processes, strategies or changes influence customer retention. For example, is it the streamlined checkout? What about the personalized product suggestions? Or maybe it was a new solution that solved their problem? Causal research will help you find out.

Discover how to use analytics to improve customer retention.

Improving problematic employee turnover rates

If your company has a high attrition rate, causal research can help you narrow down the variables or reasons which have the greatest impact on people leaving. This allows you to prioritize your efforts on tackling the issues in the right order, for the best positive outcomes.

For example, through causal research, you might find that employee dissatisfaction due to a lack of communication and transparency from upper management leads to poor morale, which in turn influences employee retention.

To rectify the problem, you could implement a routine feedback loop or session that enables your people to talk to your company’s C-level executives so that they feel heard and understood.

How to conduct causal research first steps to getting started are:

1. Define the purpose of your research

What questions do you have? What do you expect to come out of your research? Think about which variables you need to test out the theory.

2. Pick a random sampling if participants are needed

Using a technology solution to support your sampling, like a database, can help you define who you want your target audience to be, and how random or representative they should be.

3. Set up the controlled experiment

Once you’ve defined which variables you’d like to measure to see if they interact, think about how best to set up the experiment. This could be in-person or in-house via interviews, or it could be done remotely using online surveys.

4. Carry out the experiment

Make sure to keep all irrelevant variables the same, and only change the causal variable (the one that causes the effect) to gather the correct data. Depending on your method, you could be collecting qualitative or quantitative data, so make sure you note your findings across each regularly.

5. Analyze your findings

Either manually or using technology, analyze your data to see if any trends, patterns or correlations emerge. By looking at the data, you’ll be able to see what changes you might need to do next time, or if there are questions that require further research.

6. Verify your findings

Your first attempt gives you the baseline figures to compare the new results to. You can then run another experiment to verify your findings.

7. Do follow-up or supplemental research

You can supplement your original findings by carrying out research that goes deeper into causes or explores the topic in more detail. One of the best ways to do this is to use a survey. See ‘Use surveys to help your experiment’.

Identifying causal relationships between variables

To verify if a causal relationship exists, you have to satisfy the following criteria:

  • Nonspurious association

A clear correlation exists between one cause and the effect. In other words, no ‘third’ that relates to both (cause and effect) should exist.

  • Temporal sequence

The cause occurs before the effect. For example, increased ad spend on product marketing would contribute to higher product sales.

  • Concomitant variation

The variation between the two variables is systematic. For example, if a company doesn’t change its IT policies and technology stack, then changes in employee productivity were not caused by IT policies or technology.

How surveys help your causal research experiments?

There are some surveys that are perfect for assisting researchers with understanding cause and effect. These include:

  • Employee Satisfaction Survey – An introductory employee satisfaction survey that provides you with an overview of your current employee experience.
  • Manager Feedback Survey – An introductory manager feedback survey geared toward improving your skills as a leader with valuable feedback from your team.
  • Net Promoter Score (NPS) Survey – Measure customer loyalty and understand how your customers feel about your product or service using one of the world’s best-recognized metrics.
  • Employee Engagement Survey – An entry-level employee engagement survey that provides you with an overview of your current employee experience.
  • Customer Satisfaction Survey – Evaluate how satisfied your customers are with your company, including the products and services you provide and how they are treated when they buy from you.
  • Employee Exit Interview Survey – Understand why your employees are leaving and how they’ll speak about your company once they’re gone.
  • Product Research Survey – Evaluate your consumers’ reaction to a new product or product feature across every stage of the product development journey.
  • Brand Awareness Survey – Track the level of brand awareness in your target market, including current and potential future customers.
  • Online Purchase Feedback Survey – Find out how well your online shopping experience performs against customer needs and expectations.

That covers the fundamentals of causal research and should give you a foundation for ongoing studies to assess opportunities, problems, and risks across your market, product, customer, and employee segments.

If you want to transform your research, empower your teams and get insights on tap to get ahead of the competition, maybe it’s time to leverage Qualtrics CoreXM.

Qualtrics CoreXM provides a single platform for data collection and analysis across every part of your business — from customer feedback to product concept testing. What’s more, you can integrate it with your existing tools and services thanks to a flexible API.

Qualtrics CoreXM offers you as much or as little power and complexity as you need, so whether you’re running simple surveys or more advanced forms of research, it can deliver every time.

Related resources

Market intelligence 10 min read, marketing insights 11 min read, ethnographic research 11 min read, qualitative vs quantitative research 13 min read, qualitative research questions 11 min read, qualitative research design 12 min read, primary vs secondary research 14 min read, request demo.

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Causal Research: What it is, Tips & Examples

Causal research examines if there's a cause-and-effect relationship between two separate events. Learn everything you need to know about it.

Causal research is classified as conclusive research since it attempts to build a cause-and-effect link between two variables. This research is mainly used to determine the cause of particular behavior. We can use this research to determine what changes occur in an independent variable due to a change in the dependent variable.

It can assist you in evaluating marketing activities, improving internal procedures, and developing more effective business plans. Understanding how one circumstance affects another may help you determine the most effective methods for satisfying your business needs.

LEARN ABOUT: Behavioral Research

This post will explain causal research, define its essential components, describe its benefits and limitations, and provide some important tips.

Content Index

What is causal research?

Temporal sequence, non-spurious association, concomitant variation, the advantages, the disadvantages, causal research examples, causal research tips.

Causal research is also known as explanatory research . It’s a type of research that examines if there’s a cause-and-effect relationship between two separate events. This would occur when there is a change in one of the independent variables, which is causing changes in the dependent variable.

You can use causal research to evaluate the effects of particular changes on existing norms, procedures, and so on. This type of research examines a condition or a research problem to explain the patterns of interactions between variables.

LEARN ABOUT: Research Process Steps

Components of causal research

Only specific causal information can demonstrate the existence of cause-and-effect linkages. The three key components of causal research are as follows:

Causal Research Components

Prior to the effect, the cause must occur. If the cause occurs before the appearance of the effect, the cause and effect can only be linked. For example, if the profit increase occurred before the advertisement aired, it cannot be linked to an increase in advertising spending.

Linked fluctuations between two variables are only allowed if there is no other variable that is related to both cause and effect. For example, a notebook manufacturer has discovered a correlation between notebooks and the autumn season. They see that during this season, more people buy notebooks because students are buying them for the upcoming semester.

During the summer, the company launched an advertisement campaign for notebooks. To test their assumption, they can look up the campaign data to see if the increase in notebook sales was due to the student’s natural rhythm of buying notebooks or the advertisement.

Concomitant variation is defined as a quantitative change in effect that happens solely as a result of a quantitative change in the cause. This means that there must be a steady change between the two variables. You can examine the validity of a cause-and-effect connection by seeing if the independent variable causes a change in the dependent variable.

For example, if any company does not make an attempt to enhance sales by acquiring skilled employees or offering training to them, then the hire of experienced employees cannot be credited for an increase in sales. Other factors may have contributed to the increase in sales.

Causal Research Advantages and Disadvantages

Causal or explanatory research has various advantages for both academics and businesses. As with any other research method, it has a few disadvantages that researchers should be aware of. Let’s look at some of the advantages and disadvantages of this research design .

  • Helps in the identification of the causes of system processes. This allows the researcher to take the required steps to resolve issues or improve outcomes.
  • It provides replication if it is required.
  • Causal research assists in determining the effects of changing procedures and methods.
  • Subjects are chosen in a methodical manner. As a result, it is beneficial for improving internal validity .
  • The ability to analyze the effects of changes on existing events, processes, phenomena, and so on.
  • Finds the sources of variable correlations, bridging the gap in correlational research .
  • It is not always possible to monitor the effects of all external factors, so causal research is challenging to do.
  • It is time-consuming and might be costly to execute.
  • The effect of a large range of factors and variables existing in a particular setting makes it difficult to draw results.
  • The most major error in this research is a coincidence. A coincidence between a cause and an effect can sometimes be interpreted as a direction of causality.
  • To corroborate the findings of the explanatory research , you must undertake additional types of research. You can’t just make conclusions based on the findings of a causal study.
  • It is sometimes simple for a researcher to see that two variables are related, but it can be difficult for a researcher to determine which variable is the cause and which variable is the effect.

Since different industries and fields can carry out causal comparative research , it can serve many different purposes. Let’s discuss 3 examples of causal research:

Advertising Research

Companies can use causal research to enact and study advertising campaigns. For example, six months after a business debuts a new ad in a region. They see a 5% increase in sales revenue.

To assess whether the ad has caused the lift, they run the same ad in randomly selected regions so they can compare sales data across regions over another six months. When sales pick up again in these regions, they can conclude that the ad and sales have a valuable cause-and-effect relationship.

LEARN ABOUT: Ad Testing

Customer Loyalty Research

Businesses can use causal research to determine the best customer retention strategies. They monitor interactions between associates and customers to identify patterns of cause and effect, such as a product demonstration technique leading to increased or decreased sales from the same customers.

For example, a company implements a new individual marketing strategy for a small group of customers and sees a measurable increase in monthly subscriptions. After receiving identical results from several groups, they concluded that the one-to-one marketing strategy has the causal relationship they intended.

Educational Research

Learning specialists, academics, and teachers use causal research to learn more about how politics affects students and identify possible student behavior trends. For example, a university administration notices that more science students drop out of their program in their third year, which is 7% higher than in any other year.

They interview a random group of science students and discover many factors that could lead to these circumstances, including non-university components. Through the in-depth statistical analysis, researchers uncover the top three factors, and management creates a committee to address them in the future.

Causal research is frequently the last type of research done during the research process and is considered definitive. As a result, it is critical to plan the research with specific parameters and goals in mind. Here are some tips for conducting causal research successfully:

1. Understand the parameters of your research

Identify any design strategies that change the way you understand your data. Determine how you acquired data and whether your conclusions are more applicable in practice in some cases than others.

2. Pick a random sampling strategy

Choosing a technique that works best for you when you have participants or subjects is critical. You can use a database to generate a random list, select random selections from sorted categories, or conduct a survey.

3. Determine all possible relations

Examine the different relationships between your independent and dependent variables to build more sophisticated insights and conclusions.

To summarize, causal or explanatory research helps organizations understand how their current activities and behaviors will impact them in the future. This is incredibly useful in a wide range of business scenarios. This research can ensure the outcome of various marketing activities, campaigns, and collaterals. Using the findings of this research program, you will be able to design more successful business strategies that take advantage of every business opportunity.

At QuestionPro, we offer all kinds of necessary tools for researchers to carry out their projects. It can help you get the most out of your data by guiding you through the process.

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7.5: Causal Arguments

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Causal arguments attempt to make a case that one thing led to another. They answer the question "What caused it?" Causes are often complex and multiple. Before we choose a strategy for a causal argument it can help to identify our purpose. Why do we need to know the cause? How will it help us?

Purposes of causal arguments

To get a complete picture of how and why something happened.

In this case, we will want to look for multiple causes, each of which may play a different role. Some might be background conditions, others might spark the event, and others may be influences that sped up the event once it got started. In this case, we often speak of near causes that are close in time or space to the event itself, and remote causes , that are further away or further in the past. We can also describe a chain of causes , with one thing leading to the next, which leads to the next. It may even be the case that we have a feedback loop where a first event causes a second event and the second event triggers more of the first, creating an endless circle of causation. For example, as sea ice melts in the arctic, the dark water absorbs more heat, which warms it further, which melts more ice, which makes the water absorb more heat, etc. If the results are bad, this is called a vicious circle.

To decide who is responsible

Sometimes if an event has multiple causes, we may be most concerned with deciding who bears responsibility and how much. In a car accident, the driver might bear responsibility and the car manufacturer might bear some as well. We will have to argue that the responsible party caused the event but we will also have to show that there was a moral obligation not to do what the party did. That implies some degree of choice and knowledge of possible consequences. If the driver was following all good driving regulations and triggered an explosion by activating the turn signal, clearly the driver cannot be held responsible.

In order to determine that someone is responsible, there must be a clearly defined domain of responsibility for that person or entity. To convince readers that a certain party is responsible, readers have to agree on what the expectations for that party in their particular role are. For example, if a patient misreads the directions for taking a drug and accidentally overdoses, does the drug manufacturer bear any responsibility? What about the pharmacist? To decide that, we need to agree on how much responsibility the manufacturer has for making the directions foolproof and how much the pharmacist has for making sure the patient understands them. Sometimes a person can be held responsible for something they didn't do if the action omitted fell under their domain of responsibility.

To figure out how to make something happen

In this case we need to zero in on a factor or factors that will push the event forward. Such a factor is sometimes called a precipitating cause. The success of this push will depend on circumstances being right for it, so we will likely also need to describe the conditions that have to be in place for the precipitating cause to actually precipitate the event. If there are likely factors that could block the event, we need to show that those can be eliminated. For example, if we propose a particular surgery to fix a heart problem, we will also need to show that the patient can get to a hospital that performs the surgery and get an appointment. We will certainly need to show that the patient is likely to tolerate the surgery.

To stop something from happening

In this case, we do not need to describe all possible causes. We want to find a factor that is so necessary to the bad result that if we get rid of that factor, the result cannot occur. Then if we eliminate that factor, we can block the bad result. If we cannot find a single such factor, we may at least be able to find one that will make the bad result less likely. For example, to reduce wildfire risk in California, we cannot get rid of all fire whatsoever, but we can repair power lines and aging gas and electric infrastructure to reduce the risk that defects in this system will spark a fire. Or we could try to reduce the damage fires cause by focusing on clearing underbrush.

To predict what might happen in future

As Jeanne Fahnestock and Marie Secor put it in A Rhetoric of Argument, "When you argue for a prediction, you try to convince your reader that all the causes needed to bring about an event are in place or will fall into place." You also may need to show that nothing will intervene to block the event from happening. One common way to support a prediction is by comparing it to a past event that has already played out. For example, we might argue that humans have survived natural disasters in the past, so we will survive the effects of climate change as well. As Fahnestock and Secor point out, however, "the argument is only as good as the analogy, which sometimes must itself be supported." How comparable are the disasters of the past to the likely effects of climate change? The argument would need to describe both past and possible future events and convince us that they are similar in severity.

Techniques and cautions for causal argument

So how does a writer make a case that one thing causes another? The briefest answer is that the writer needs to convince us that the factor and the event are correlated and also that there is some way in which the factor could plausibly lead to the event. Then the writer will need to convince us that they have done due diligence in considering and eliminating alternate possibilities for the cause and alternate explanations for any correlation between the factor and the event.

Identify possible causes

If other writers have already identified possible causes, an argument simply needs to refer back to those and add in any that have been missed. If not, the writer can put themselves in the role of detective and imagine what might have caused the event.

Determine which factor is most correlated with the event

If we think that a factor may commonly cause an event, the first question to ask is whether they go together. If we are looking for a sole cause, we can ask if the factor is always there when the event happens and always absent when the event doesn't happen. Do the factor and the event follow the same trends? The following methods of arguing for causality were developed by philosopher John Stuart Mill, and are often referred to as "Mill's methods."

  • If the event is repeated and every time it happens, a common factor is present, that common factor may be the cause.
  • If there is a single difference between cases where the event takes place and cases where it doesn't.
  • If an event and a possible cause are repeated over and over and they happen to varying degrees, we can check whether they always increase and decrease together. This is often best done with a graph so we can visually check whether the lines follow the same pattern.
  • Finally, ruling out other possible causes can support a case that the one remaining possible cause did in fact operate.

Explain how that factor could have caused the event

In order to believe that one thing caused another, we usually need to have some idea of how the first thing could cause the second. If we cannot imagine how one would cause another, why should we find it plausible? Any argument about agency , or the way in which one thing caused another, depends on assumptions about what makes things happen. If we are talking about human behavior, then we are looking for motivation: love, hate, envy, greed, desire for power, etc. If we are talking about a physical event, then we need to look at physical forces. Scientists have dedicated much research to establishing how carbon dioxide in the atmosphere could effectively trap heat and warm the planet.

If there is enough other evidence to show that one thing caused another but the way it happened is still unknown, the argument can note that and perhaps point toward further studies that would establish the mechanism. The writer may want to qualify their argument with "may" or "might" or "seems to indicate," if they cannot explain how the supposed cause led to the effect.

Eliminate alternate explanations

The catchphrase " correlation is not causation " can help us to remember the dangers of the methods above. It's usually easy to show that two things happen at the same time or in the same pattern, but hard to show that one actually causes another. Correlation can be a good reason to investigate whether something is the cause, and it can provide some evidence of causality, but it is not proof. Sometimes two unrelated things may be correlated, like the number of women in Congress and the price of milk. We can imagine that both might follow an upward trend, one because of the increasing equality of women in society and the other because of inflation. Describing a plausible agency, or way in which one thing led to another, can help show that the correlation is not random. If we find a strong correlation, we can imagine various causal arguments that would explain it and argue that the one we support has the most plausible agency.

Sometimes things vary together because there is a common cause that affects both of them. An argument can explore possible third factors that may have led to both events. For example, students who go to elite colleges tend to make more money than students who go to less elite colleges. Did the elite colleges make the difference? Or are both the college choice and the later earnings due to a third cause, such as family connections? In his book Food Rules: An Eater's Manual, journalist Michael Pollan assesses studies on the effects of supplements like multivitamins and concludes that people who take supplements are also those who have better diet and exercise habits, and that the supplements themselves have no effect on health. He advises, “Be the kind of person who takes supplements -- then skip the supplements.”

If we have two phenomena that are correlated and happen at the same time, it's worth considering whether the second phenomenon could actually have caused the first rather than the other way around. For example, if we find that gun violence and violence within video games are both on the rise, we shouldn't leap to blame video games for the increase in shootings. It may be that people who play video games are being influenced by violence in the games and becoming more likely to go out and shoot people in real life. But could it also be that as gun violence increases in society for other reasons, such violence is a bigger part of people's consciousness, leading video game makers and gamers to incorporate more violence in their games? It might be that causality operates in both directions, creating a feedback loop as we discussed above.

Proving causality is tricky, and often even rigorous academic studies can do little more than suggest that causality is probable or possible. There are a host of laboratory and statistical methods for testing causality. The gold standard for an experiment to determine a cause is a double-blind, randomized control trial in which there are two groups of people randomly assigned. One group gets the drug being studied and one group gets the placebo, but neither the participants nor the researchers know which is which. This kind of study eliminates the effect of unconscious suggestion, but it is often not possible for ethical and logistical reasons.

The ins and outs of causal arguments are worth studying in a statistics course or a philosophy course, but even without such a course we can do a better job of assessing causes if we develop the habit of looking for alternate explanations.

Sample annotated causal argument

The article "Climate Explained: Why Carbon Dioxide Has Such Outsized Influence on Earth’s Climate" by Jason West, published in The Conversation , can serve as an example. Annotations point out how the author uses several causal argument strategies.    

  • Sample causal essay "Climate Explained: Why Carbon Dioxide Has Such Outsized Influence on Earth’s Climate" in PDF version with margin notes
  • Sample causal essay "Climate Explained: Why Carbon Dioxide Has Such Outsized Influence on Earth’s Climate" accessible version with notes in parentheses

Practice Exercise \(\PageIndex{1}\)

Reflect on the following to construct a causal argument. What would be the best intervention to introduce in society to reduce the rate of violent crime? Below are some possible causes of violent crime.  Choose one and describe how it could lead to violent crime.  Then think of a way to intervene in that process to stop it.  What method from among those described in this section would you use to convince someone that your intervention would work to lower rates of violent crime?  Make up an argument using your chosen method and the kind of evidence, either anecdotal or statistical, you would find convincing.

Possible causes of violent crime:

  • Homophobia and transphobia
  • Testosterone
  • Child abuse
  • Violence in the media
  • Role models who exhibit toxic masculinity
  • Violent video games
  • Systemic racism
  • Lack of education on expressing emotions
  • Unemployment
  • Not enough law enforcement
  • Economic inequality 
  • The availability of guns

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What is causal research design?

Last updated

14 May 2023

Reviewed by

Examining these relationships gives researchers valuable insights into the mechanisms that drive the phenomena they are investigating.

Organizations primarily use causal research design to identify, determine, and explore the impact of changes within an organization and the market. You can use a causal research design to evaluate the effects of certain changes on existing procedures, norms, and more.

This article explores causal research design, including its elements, advantages, and disadvantages.

Analyze your causal research

Dovetail streamlines causal research analysis to help you uncover and share actionable insights

  • Components of causal research

You can demonstrate the existence of cause-and-effect relationships between two factors or variables using specific causal information, allowing you to produce more meaningful results and research implications.

These are the key inputs for causal research:

The timeline of events

Ideally, the cause must occur before the effect. You should review the timeline of two or more separate events to determine the independent variables (cause) from the dependent variables (effect) before developing a hypothesis. 

If the cause occurs before the effect, you can link cause and effect and develop a hypothesis .

For instance, an organization may notice a sales increase. Determining the cause would help them reproduce these results. 

Upon review, the business realizes that the sales boost occurred right after an advertising campaign. The business can leverage this time-based data to determine whether the advertising campaign is the independent variable that caused a change in sales. 

Evaluation of confounding variables

In most cases, you need to pinpoint the variables that comprise a cause-and-effect relationship when using a causal research design. This uncovers a more accurate conclusion. 

Co-variations between a cause and effect must be accurate, and a third factor shouldn’t relate to cause and effect. 

Observing changes

Variation links between two variables must be clear. A quantitative change in effect must happen solely due to a quantitative change in the cause. 

You can test whether the independent variable changes the dependent variable to evaluate the validity of a cause-and-effect relationship. A steady change between the two variables must occur to back up your hypothesis of a genuine causal effect. 

  • Why is causal research useful?

Causal research allows market researchers to predict hypothetical occurrences and outcomes while enhancing existing strategies. Organizations can use this concept to develop beneficial plans. 

Causal research is also useful as market researchers can immediately deduce the effect of the variables on each other under real-world conditions. 

Once researchers complete their first experiment, they can use their findings. Applying them to alternative scenarios or repeating the experiment to confirm its validity can produce further insights. 

Businesses widely use causal research to identify and comprehend the effect of strategic changes on their profits. 

  • How does causal research compare and differ from other research types?

Other research types that identify relationships between variables include exploratory and descriptive research . 

Here’s how they compare and differ from causal research designs:

Exploratory research

An exploratory research design evaluates situations where a problem or opportunity's boundaries are unclear. You can use this research type to test various hypotheses and assumptions to establish facts and understand a situation more clearly.

You can also use exploratory research design to navigate a topic and discover the relevant variables. This research type allows flexibility and adaptability as the experiment progresses, particularly since no area is off-limits.

It’s worth noting that exploratory research is unstructured and typically involves collecting qualitative data . This provides the freedom to tweak and amend the research approach according to your ongoing thoughts and assessments. 

Unfortunately, this exposes the findings to the risk of bias and may limit the extent to which a researcher can explore a topic. 

This table compares the key characteristics of causal and exploratory research:

Descriptive research

This research design involves capturing and describing the traits of a population, situation, or phenomenon. Descriptive research focuses more on the " what " of the research subject and less on the " why ."

Since descriptive research typically happens in a real-world setting, variables can cross-contaminate others. This increases the challenge of isolating cause-and-effect relationships. 

You may require further research if you need more causal links. 

This table compares the key characteristics of causal and descriptive research.  

Causal research examines a research question’s variables and how they interact. It’s easier to pinpoint cause and effect since the experiment often happens in a controlled setting. 

Researchers can conduct causal research at any stage, but they typically use it once they know more about the topic.

In contrast, causal research tends to be more structured and can be combined with exploratory and descriptive research to help you attain your research goals. 

  • How can you use causal research effectively?

Here are common ways that market researchers leverage causal research effectively:

Market and advertising research

Do you want to know if your new marketing campaign is affecting your organization positively? You can use causal research to determine the variables causing negative or positive impacts on your campaign. 

Improving customer experiences and loyalty levels

Consumers generally enjoy purchasing from brands aligned with their values. They’re more likely to purchase from such brands and positively represent them to others. 

You can use causal research to identify the variables contributing to increased or reduced customer acquisition and retention rates. 

Could the cause of increased customer retention rates be streamlined checkout? 

Perhaps you introduced a new solution geared towards directly solving their immediate problem. 

Whatever the reason, causal research can help you identify the cause-and-effect relationship. You can use this to enhance your customer experiences and loyalty levels.

Improving problematic employee turnover rates

Is your organization experiencing skyrocketing attrition rates? 

You can leverage the features and benefits of causal research to narrow down the possible explanations or variables with significant effects on employees quitting. 

This way, you can prioritize interventions, focusing on the highest priority causal influences, and begin to tackle high employee turnover rates. 

  • Advantages of causal research

The main benefits of causal research include the following:

Effectively test new ideas

If causal research can pinpoint the precise outcome through combinations of different variables, researchers can test ideas in the same manner to form viable proof of concepts.

Achieve more objective results

Market researchers typically use random sampling techniques to choose experiment participants or subjects in causal research. This reduces the possibility of exterior, sample, or demography-based influences, generating more objective results. 

Improved business processes

Causal research helps businesses understand which variables positively impact target variables, such as customer loyalty or sales revenues. This helps them improve their processes, ROI, and customer and employee experiences.

Guarantee reliable and accurate results

Upon identifying the correct variables, researchers can replicate cause and effect effortlessly. This creates reliable data and results to draw insights from. 

Internal organization improvements

Businesses that conduct causal research can make informed decisions about improving their internal operations and enhancing employee experiences. 

  • Disadvantages of causal research

Like any other research method, casual research has its set of drawbacks that include:

Extra research to ensure validity

Researchers can't simply rely on the outcomes of causal research since it isn't always accurate. There may be a need to conduct other research types alongside it to ensure accurate output.

Coincidence

Coincidence tends to be the most significant error in causal research. Researchers often misinterpret a coincidental link between a cause and effect as a direct causal link. 

Administration challenges

Causal research can be challenging to administer since it's impossible to control the impact of extraneous variables . 

Giving away your competitive advantage

If you intend to publish your research, it exposes your information to the competition. 

Competitors may use your research outcomes to identify your plans and strategies to enter the market before you. 

  • Causal research examples

Multiple fields can use causal research, so it serves different purposes, such as. 

Customer loyalty research

Organizations and employees can use causal research to determine the best customer attraction and retention approaches. 

They monitor interactions between customers and employees to identify cause-and-effect patterns. That could be a product demonstration technique resulting in higher or lower sales from the same customers. 

Example: Business X introduces a new individual marketing strategy for a small customer group and notices a measurable increase in monthly subscriptions. 

Upon getting identical results from different groups, the business concludes that the individual marketing strategy resulted in the intended causal relationship.

Advertising research

Businesses can also use causal research to implement and assess advertising campaigns. 

Example: Business X notices a 7% increase in sales revenue a few months after a business introduces a new advertisement in a certain region. The business can run the same ad in random regions to compare sales data over the same period. 

This will help the company determine whether the ad caused the sales increase. If sales increase in these randomly selected regions, the business could conclude that advertising campaigns and sales share a cause-and-effect relationship. 

Educational research

Academics, teachers, and learners can use causal research to explore the impact of politics on learners and pinpoint learner behavior trends. 

Example: College X notices that more IT students drop out of their program in their second year, which is 8% higher than any other year. 

The college administration can interview a random group of IT students to identify factors leading to this situation, including personal factors and influences. 

With the help of in-depth statistical analysis, the institution's researchers can uncover the main factors causing dropout. They can create immediate solutions to address the problem.

Is a causal variable dependent or independent?

When two variables have a cause-and-effect relationship, the cause is often called the independent variable. As such, the effect variable is dependent, i.e., it depends on the independent causal variable. An independent variable is only causal under experimental conditions. 

What are the three criteria for causality?

The three conditions for causality are:

Temporality/temporal precedence: The cause must precede the effect.

Rationality: One event predicts the other with an explanation, and the effect must vary in proportion to changes in the cause.

Control for extraneous variables: The covariables must not result from other variables.  

Is causal research experimental?

Causal research is mostly explanatory. Causal studies focus on analyzing a situation to explore and explain the patterns of relationships between variables. 

Further, experiments are the primary data collection methods in studies with causal research design. However, as a research design, causal research isn't entirely experimental.

What is the difference between experimental and causal research design?

One of the main differences between causal and experimental research is that in causal research, the research subjects are already in groups since the event has already happened. 

On the other hand, researchers randomly choose subjects in experimental research before manipulating the variables.

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Methodology

  • Correlation vs. Causation | Difference, Designs & Examples

Correlation vs. Causation | Difference, Designs & Examples

Published on July 12, 2021 by Pritha Bhandari . Revised on June 22, 2023.

Correlation means there is a statistical association between variables. Causation means that a change in one variable causes a change in another variable.

In research, you might have come across the phrase “correlation doesn’t imply causation.” Correlation and causation are two related ideas, but understanding their differences will help you critically evaluate sources and interpret scientific research.

Table of contents

What’s the difference, why doesn’t correlation mean causation, correlational research, third variable problem, regression to the mean, spurious correlations, directionality problem, causal research, other interesting articles, frequently asked questions about correlation and causation.

Correlation describes an association between types of variables : when one variable changes, so does the other. A correlation is a statistical indicator of the relationship between variables. These variables change together: they covary. But this covariation isn’t necessarily due to a direct or indirect causal link.

Causation means that changes in one variable brings about changes in the other; there is a cause-and-effect relationship between variables. The two variables are correlated with each other and there is also a causal link between them.

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There are two main reasons why correlation isn’t causation. These problems are important to identify for drawing sound scientific conclusions from research.

The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not. For example, ice cream sales and violent crime rates are closely correlated, but they are not causally linked with each other. Instead, hot temperatures, a third variable, affects both variables separately. Failing to account for third variables can lead research biases to creep into your work.

The directionality problem occurs when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other. For example, vitamin D levels are correlated with depression, but it’s not clear whether low vitamin D causes depression, or whether depression causes reduced vitamin D intake.

You’ll need to use an appropriate research design to distinguish between correlational and causal relationships:

  • Correlational research designs can only demonstrate correlational links between variables.
  • Experimental designs can test causation.

In a correlational research design, you collect data on your variables without manipulating them.

Correlational research is usually high in external validity , so you can generalize your findings to real life settings. But these studies are low in internal validity , which makes it difficult to causally connect changes in one variable to changes in the other.

These research designs are commonly used when it’s unethical, too costly, or too difficult to perform controlled experiments. They are also used to study relationships that aren’t expected to be causal.

Without controlled experiments, it’s hard to say whether it was the variable you’re interested in that caused changes in another variable. Extraneous variables are any third variable or omitted variable other than your variables of interest that could affect your results.

Limited control in correlational research means that extraneous or confounding variables serve as alternative explanations for the results. Confounding variables can make it seem as though a correlational relationship is causal when it isn’t.

When two variables are correlated, all you can say is that changes in one variable occur alongside changes in the other.

Regression to the mean is observed when variables that are extremely higher or extremely lower than average on the first measurement move closer to the average on the second measurement. Particularly in research that intentionally focuses on the most extreme cases or events, RTM should always be considered as a possible cause of an observed change.

Players or teams featured on the cover of SI have earned their place by performing exceptionally well. But athletic success is a mix of skill and luck, and even the best players don’t always win.

Chances are that good luck will not continue indefinitely, and neither can exceptional success.

A spurious correlation is when two variables appear to be related through hidden third variables or simply by coincidence.

The Theory of the Stork draws a simple causal link between the variables to argue that storks physically deliver babies. This satirical study shows why you can’t conclude causation from correlational research alone.

When you analyze correlations in a large dataset with many variables, the chances of finding at least one statistically significant result are high. In this case, you’re more likely to make a type I error . This means erroneously concluding there is a true correlation between variables in the population based on skewed sample data.

To demonstrate causation, you need to show a directional relationship with no alternative explanations. This relationship can be unidirectional, with one variable impacting the other, or bidirectional, where both variables impact each other.

A correlational design won’t be able to distinguish between any of these possibilities, but an experimental design can test each possible direction, one at a time.

  • Physical activity may affect self esteem
  • Self esteem may affect physical activity
  • Physical activity and self esteem may both affect each other

In correlational research, the directionality of a relationship is unclear because there is limited researcher control. You might risk concluding reverse causality, the wrong direction of the relationship.

Causal links between variables can only be truly demonstrated with controlled experiments . Experiments test formal predictions, called hypotheses , to establish causality in one direction at a time.

Experiments are high in internal validity , so cause-and-effect relationships can be demonstrated with reasonable confidence.

You can establish directionality in one direction because you manipulate an independent variable before measuring the change in a dependent variable.

In a controlled experiment, you can also eliminate the influence of third variables by using random assignment and control groups.

Random assignment helps distribute participant characteristics evenly between groups so that they’re similar and comparable. A control group lets you compare the experimental manipulation to a similar treatment or no treatment (or a placebo, to control for the placebo effect ).

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

  • Chi square test of independence
  • Statistical power
  • Descriptive statistics
  • Degrees of freedom
  • Pearson correlation
  • Null hypothesis
  • Double-blind study
  • Case-control study
  • Research ethics
  • Data collection
  • Hypothesis testing
  • Structured interviews

Research bias

  • Hawthorne effect
  • Unconscious bias
  • Recall bias
  • Halo effect
  • Self-serving bias
  • Information bias

A correlation reflects the strength and/or direction of the association between two or more variables.

  • A positive correlation means that both variables change in the same direction.
  • A negative correlation means that the variables change in opposite directions.
  • A zero correlation means there’s no relationship between the variables.

Correlation describes an association between variables : when one variable changes, so does the other. A correlation is a statistical indicator of the relationship between variables.

Causation means that changes in one variable brings about changes in the other (i.e., there is a cause-and-effect relationship between variables). The two variables are correlated with each other, and there’s also a causal link between them.

While causation and correlation can exist simultaneously, correlation does not imply causation. In other words, correlation is simply a relationship where A relates to B—but A doesn’t necessarily cause B to happen (or vice versa). Mistaking correlation for causation is a common error and can lead to false cause fallacy .

The third variable and directionality problems are two main reasons why correlation isn’t causation .

The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not.

The directionality problem is when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other.

Controlled experiments establish causality, whereas correlational studies only show associations between variables.

  • In an experimental design , you manipulate an independent variable and measure its effect on a dependent variable. Other variables are controlled so they can’t impact the results.
  • In a correlational design , you measure variables without manipulating any of them. You can test whether your variables change together, but you can’t be sure that one variable caused a change in another.

In general, correlational research is high in external validity while experimental research is high in internal validity .

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67 Causal Essay Topics to Consider

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  • Writing Essays
  • Writing Research Papers
  • English Grammar
  • M.Ed., Education Administration, University of Georgia
  • B.A., History, Armstrong State University

A causal essay is much like a cause-and-effect essay , but there may be a subtle difference in the minds of some instructors who use the term "causal essay" for complex topics and "cause-and-effect essay" for smaller or more straightforward papers.

However, both terms describe essentially the same type of essay and the goal of each is the same: to come up with a list of events or factors (causes) that bring about a certain outcome (effect). The key question in such an essay is, "How or why did something happen?" It is important to make a clear connection between each cause and the ultimate effect.

Potential Causes

The most common problem students face in writing the causal essay is running out of "causes" to talk about. It is helpful to sketch out an outline before you begin writing the first draft of your outline. Your essay should include a strong introduction , good transition statements , and a well-crafted conclusion.

Topics to Consider

You can use a topic from this list, or use the list as inspiration for your own idea.

  • What conditions and events led to the Great Depression ?
  • What prompts a change in fashion trends?
  • Why do some people fear the dark?
  • How did some dinosaurs leave footprints?
  • What causes criminal behavior?
  • What causes people to rebel against authority?
  • What conditions lead to powerful hurricanes?
  • What developments led to regional accents in the United States?
  • Why do good students become truant?
  • What causes war?
  • What factors can lead to birth defects?
  • How are car insurance rates determined?
  • What factors can lead to obesity?
  • What can cause evolution to occur?
  • Why does unemployment rise?
  • Why do some people develop multiple personalities?
  • How does the structure of the Earth change over time?
  • What factors can cause bulimia nervosa?
  • What makes a marriage fail?
  • What developments and conditions led to the Declaration of Independence ?
  • What led to the decline of the automobile industry?
  • What factors led to the decline of the Roman Empire?
  • How did the Grand Canyon form?
  • Why did enslavement replace indentured servitude in the American colonies ?
  • How has popular music been affected by technology?
  • How has racial tolerance changed over time?
  • What led to the dot-com bubble burst?
  • What causes the stock market to fall?
  • How does scarring occur?
  • How does soap work?
  • What causes a surge in nationalism?
  • Why do some bridges collapse?
  • Why was Abraham Lincoln assassinated ?
  • How did we get the various versions of the Bible?
  • What factors led to unionization?
  • How does a tsunami form?
  • What events and factors led to women's suffrage?
  • Why did electric cars fail initially?
  • How do animals become extinct?
  • Why are some tornadoes more destructive than others?
  • What factors led to the end of feudalism?
  • What led to the " Martian Panic " in the 1930s?
  • How did medicine change in the 19th century?
  • How does gene therapy work?
  • What factors can lead to famine?
  • What factors led to the rise of democratic governments in the 18th century?
  • How did baseball become a national pastime in the United States?
  • What was the impact of Jim Crow laws on Black citizens in the United States?
  • What factors led to the growth of imperialism?
  • Why did the Salem witch trials take place?
  • How did Adolf Hitler come to power?
  • What can cause damage to your credit?
  • How did the conservationism start?
  • How did World War I start?
  • How do germs spread and cause illness?
  • How do people lose weight?
  • How does road salt prevent accidents?
  • What makes some tires grip better than others?
  • What makes a computer run slowly?
  • How does a car work?
  • How has the news industry changed over time?
  • What created Beatlemania ?
  • How did organized crime develop?
  • What caused the obesity epidemic?
  • How did grammar rules develop in the English language?
  • Where do political parties come from?
  • How did the Civil Rights movement begin?
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Causal Research: Definition, Design, Tips, Examples

Appinio Research · 21.02.2024 · 33min read

Causal Research Definition Design Tips Examples

Ever wondered why certain events lead to specific outcomes? Understanding causality—the relationship between cause and effect—is crucial for unraveling the mysteries of the world around us. In this guide on causal research, we delve into the methods, techniques, and principles behind identifying and establishing cause-and-effect relationships between variables. Whether you're a seasoned researcher or new to the field, this guide will equip you with the knowledge and tools to conduct rigorous causal research and draw meaningful conclusions that can inform decision-making and drive positive change.

What is Causal Research?

Causal research is a methodological approach used in scientific inquiry to investigate cause-and-effect relationships between variables. Unlike correlational or descriptive research, which merely examine associations or describe phenomena, causal research aims to determine whether changes in one variable cause changes in another variable.

Importance of Causal Research

Understanding the importance of causal research is crucial for appreciating its role in advancing knowledge and informing decision-making across various fields. Here are key reasons why causal research is significant:

  • Establishing Causality:  Causal research enables researchers to determine whether changes in one variable directly cause changes in another variable. This helps identify effective interventions, predict outcomes, and inform evidence-based practices.
  • Guiding Policy and Practice:  By identifying causal relationships, causal research provides empirical evidence to support policy decisions, program interventions, and business strategies. Decision-makers can use causal findings to allocate resources effectively and address societal challenges.
  • Informing Predictive Modeling:  Causal research contributes to the development of predictive models by elucidating causal mechanisms underlying observed phenomena. Predictive models based on causal relationships can accurately forecast future outcomes and trends.
  • Advancing Scientific Knowledge:  Causal research contributes to the cumulative body of scientific knowledge by testing hypotheses, refining theories, and uncovering underlying mechanisms of phenomena. It fosters a deeper understanding of complex systems and phenomena.
  • Mitigating Confounding Factors:  Understanding causal relationships allows researchers to control for confounding variables and reduce bias in their studies. By isolating the effects of specific variables, researchers can draw more valid and reliable conclusions.

Causal Research Distinction from Other Research

Understanding the distinctions between causal research and other types of research methodologies is essential for researchers to choose the most appropriate approach for their study objectives. Let's explore the differences and similarities between causal research and descriptive, exploratory, and correlational research methodologies .

Descriptive vs. Causal Research

Descriptive research  focuses on describing characteristics, behaviors, or phenomena without manipulating variables or establishing causal relationships. It provides a snapshot of the current state of affairs but does not attempt to explain why certain phenomena occur.

Causal research , on the other hand, seeks to identify cause-and-effect relationships between variables by systematically manipulating independent variables and observing their effects on dependent variables. Unlike descriptive research, causal research aims to determine whether changes in one variable directly cause changes in another variable.

Similarities:

  • Both descriptive and causal research involve empirical observation and data collection.
  • Both types of research contribute to the scientific understanding of phenomena, albeit through different approaches.

Differences:

  • Descriptive research focuses on describing phenomena, while causal research aims to explain why phenomena occur by identifying causal relationships.
  • Descriptive research typically uses observational methods, while causal research often involves experimental designs or causal inference techniques to establish causality.

Exploratory vs. Causal Research

Exploratory research  aims to explore new topics, generate hypotheses, or gain initial insights into phenomena. It is often conducted when little is known about a subject and seeks to generate ideas for further investigation.

Causal research , on the other hand, is concerned with testing hypotheses and establishing cause-and-effect relationships between variables. It builds on existing knowledge and seeks to confirm or refute causal hypotheses through systematic investigation.

  • Both exploratory and causal research contribute to the generation of knowledge and theory development.
  • Both types of research involve systematic inquiry and data analysis to answer research questions.
  • Exploratory research focuses on generating hypotheses and exploring new areas of inquiry, while causal research aims to test hypotheses and establish causal relationships.
  • Exploratory research is more flexible and open-ended, while causal research follows a more structured and hypothesis-driven approach.

Correlational vs. Causal Research

Correlational research  examines the relationship between variables without implying causation. It identifies patterns of association or co-occurrence between variables but does not establish the direction or causality of the relationship.

Causal research , on the other hand, seeks to establish cause-and-effect relationships between variables by systematically manipulating independent variables and observing their effects on dependent variables. It goes beyond mere association to determine whether changes in one variable directly cause changes in another variable.

  • Both correlational and causal research involve analyzing relationships between variables.
  • Both types of research contribute to understanding the nature of associations between variables.
  • Correlational research focuses on identifying patterns of association, while causal research aims to establish causal relationships.
  • Correlational research does not manipulate variables, while causal research involves systematically manipulating independent variables to observe their effects on dependent variables.

How to Formulate Causal Research Hypotheses?

Crafting research questions and hypotheses is the foundational step in any research endeavor. Defining your variables clearly and articulating the causal relationship you aim to investigate is essential. Let's explore this process further.

1. Identify Variables

Identifying variables involves recognizing the key factors you will manipulate or measure in your study. These variables can be classified into independent, dependent, and confounding variables.

  • Independent Variable (IV):  This is the variable you manipulate or control in your study. It is the presumed cause that you want to test.
  • Dependent Variable (DV):  The dependent variable is the outcome or response you measure. It is affected by changes in the independent variable.
  • Confounding Variables:  These are extraneous factors that may influence the relationship between the independent and dependent variables, leading to spurious correlations or erroneous causal inferences. Identifying and controlling for confounding variables is crucial for establishing valid causal relationships.

2. Establish Causality

Establishing causality requires meeting specific criteria outlined by scientific methodology. While correlation between variables may suggest a relationship, it does not imply causation. To establish causality, researchers must demonstrate the following:

  • Temporal Precedence:  The cause must precede the effect in time. In other words, changes in the independent variable must occur before changes in the dependent variable.
  • Covariation of Cause and Effect:  Changes in the independent variable should be accompanied by corresponding changes in the dependent variable. This demonstrates a consistent pattern of association between the two variables.
  • Elimination of Alternative Explanations:  Researchers must rule out other possible explanations for the observed relationship between variables. This involves controlling for confounding variables and conducting rigorous experimental designs to isolate the effects of the independent variable.

3. Write Clear and Testable Hypotheses

Hypotheses serve as tentative explanations for the relationship between variables and provide a framework for empirical testing. A well-formulated hypothesis should be:

  • Specific:  Clearly state the expected relationship between the independent and dependent variables.
  • Testable:  The hypothesis should be capable of being empirically tested through observation or experimentation.
  • Falsifiable:  There should be a possibility of proving the hypothesis false through empirical evidence.

For example, a hypothesis in a study examining the effect of exercise on weight loss could be: "Increasing levels of physical activity (IV) will lead to greater weight loss (DV) among participants (compared to those with lower levels of physical activity)."

By formulating clear hypotheses and operationalizing variables, researchers can systematically investigate causal relationships and contribute to the advancement of scientific knowledge.

Causal Research Design

Designing your research study involves making critical decisions about how you will collect and analyze data to investigate causal relationships.

Experimental vs. Observational Designs

One of the first decisions you'll make when designing a study is whether to employ an experimental or observational design. Each approach has its strengths and limitations, and the choice depends on factors such as the research question, feasibility , and ethical considerations.

  • Experimental Design: In experimental designs, researchers manipulate the independent variable and observe its effects on the dependent variable while controlling for confounding variables. Random assignment to experimental conditions allows for causal inferences to be drawn. Example: A study testing the effectiveness of a new teaching method on student performance by randomly assigning students to either the experimental group (receiving the new teaching method) or the control group (receiving the traditional method).
  • Observational Design: Observational designs involve observing and measuring variables without intervention. Researchers may still examine relationships between variables but cannot establish causality as definitively as in experimental designs. Example: A study observing the association between socioeconomic status and health outcomes by collecting data on income, education level, and health indicators from a sample of participants.

Control and Randomization

Control and randomization are crucial aspects of experimental design that help ensure the validity of causal inferences.

  • Control: Controlling for extraneous variables involves holding constant factors that could influence the dependent variable, except for the independent variable under investigation. This helps isolate the effects of the independent variable. Example: In a medication trial, controlling for factors such as age, gender, and pre-existing health conditions ensures that any observed differences in outcomes can be attributed to the medication rather than other variables.
  • Randomization: Random assignment of participants to experimental conditions helps distribute potential confounders evenly across groups, reducing the likelihood of systematic biases and allowing for causal conclusions. Example: Randomly assigning patients to treatment and control groups in a clinical trial ensures that both groups are comparable in terms of baseline characteristics, minimizing the influence of extraneous variables on treatment outcomes.

Internal and External Validity

Two key concepts in research design are internal validity and external validity, which relate to the credibility and generalizability of study findings, respectively.

  • Internal Validity: Internal validity refers to the extent to which the observed effects can be attributed to the manipulation of the independent variable rather than confounding factors. Experimental designs typically have higher internal validity due to their control over extraneous variables. Example: A study examining the impact of a training program on employee productivity would have high internal validity if it could confidently attribute changes in productivity to the training intervention.
  • External Validity: External validity concerns the extent to which study findings can be generalized to other populations, settings, or contexts. While experimental designs prioritize internal validity, they may sacrifice external validity by using highly controlled conditions that do not reflect real-world scenarios. Example: Findings from a laboratory study on memory retention may have limited external validity if the experimental tasks and conditions differ significantly from real-life learning environments.

Types of Experimental Designs

Several types of experimental designs are commonly used in causal research, each with its own strengths and applications.

  • Randomized Control Trials (RCTs): RCTs are considered the gold standard for assessing causality in research. Participants are randomly assigned to experimental and control groups, allowing researchers to make causal inferences. Example: A pharmaceutical company testing a new drug's efficacy would use an RCT to compare outcomes between participants receiving the drug and those receiving a placebo.
  • Quasi-Experimental Designs: Quasi-experimental designs lack random assignment but still attempt to establish causality by controlling for confounding variables through design or statistical analysis. Example: A study evaluating the effectiveness of a smoking cessation program might compare outcomes between participants who voluntarily enroll in the program and a matched control group of non-enrollees.

By carefully selecting an appropriate research design and addressing considerations such as control, randomization, and validity, researchers can conduct studies that yield credible evidence of causal relationships and contribute valuable insights to their field of inquiry.

Causal Research Data Collection

Collecting data is a critical step in any research study, and the quality of the data directly impacts the validity and reliability of your findings.

Choosing Measurement Instruments

Selecting appropriate measurement instruments is essential for accurately capturing the variables of interest in your study. The choice of measurement instrument depends on factors such as the nature of the variables, the target population , and the research objectives.

  • Surveys :  Surveys are commonly used to collect self-reported data on attitudes, opinions, behaviors, and demographics . They can be administered through various methods, including paper-and-pencil surveys, online surveys, and telephone interviews.
  • Observations:  Observational methods involve systematically recording behaviors, events, or phenomena as they occur in natural settings. Observations can be structured (following a predetermined checklist) or unstructured (allowing for flexible data collection).
  • Psychological Tests:  Psychological tests are standardized instruments designed to measure specific psychological constructs, such as intelligence, personality traits, or emotional functioning. These tests often have established reliability and validity.
  • Physiological Measures:  Physiological measures, such as heart rate, blood pressure, or brain activity, provide objective data on bodily processes. They are commonly used in health-related research but require specialized equipment and expertise.
  • Existing Databases:  Researchers may also utilize existing datasets, such as government surveys, public health records, or organizational databases, to answer research questions. Secondary data analysis can be cost-effective and time-saving but may be limited by the availability and quality of data.

Ensuring accurate data collection is the cornerstone of any successful research endeavor. With the right tools in place, you can unlock invaluable insights to drive your causal research forward. From surveys to tests, each instrument offers a unique lens through which to explore your variables of interest.

At Appinio , we understand the importance of robust data collection methods in informing impactful decisions. Let us empower your research journey with our intuitive platform, where you can effortlessly gather real-time consumer insights to fuel your next breakthrough.   Ready to take your research to the next level? Book a demo today and see how Appinio can revolutionize your approach to data collection!

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Sampling Techniques

Sampling involves selecting a subset of individuals or units from a larger population to participate in the study. The goal of sampling is to obtain a representative sample that accurately reflects the characteristics of the population of interest.

  • Probability Sampling:  Probability sampling methods involve randomly selecting participants from the population, ensuring that each member of the population has an equal chance of being included in the sample. Common probability sampling techniques include simple random sampling , stratified sampling, and cluster sampling.
  • Non-Probability Sampling:  Non-probability sampling methods do not involve random selection and may introduce biases into the sample. Examples of non-probability sampling techniques include convenience sampling, purposive sampling, and snowball sampling.

The choice of sampling technique depends on factors such as the research objectives, population characteristics, resources available, and practical constraints. Researchers should strive to minimize sampling bias and maximize the representativeness of the sample to enhance the generalizability of their findings.

Ethical Considerations

Ethical considerations are paramount in research and involve ensuring the rights, dignity, and well-being of research participants. Researchers must adhere to ethical principles and guidelines established by professional associations and institutional review boards (IRBs).

  • Informed Consent:  Participants should be fully informed about the nature and purpose of the study, potential risks and benefits, their rights as participants, and any confidentiality measures in place. Informed consent should be obtained voluntarily and without coercion.
  • Privacy and Confidentiality:  Researchers should take steps to protect the privacy and confidentiality of participants' personal information. This may involve anonymizing data, securing data storage, and limiting access to identifiable information.
  • Minimizing Harm:  Researchers should mitigate any potential physical, psychological, or social harm to participants. This may involve conducting risk assessments, providing appropriate support services, and debriefing participants after the study.
  • Respect for Participants:  Researchers should respect participants' autonomy, diversity, and cultural values. They should seek to foster a trusting and respectful relationship with participants throughout the research process.
  • Publication and Dissemination:  Researchers have a responsibility to accurately report their findings and acknowledge contributions from participants and collaborators. They should adhere to principles of academic integrity and transparency in disseminating research results.

By addressing ethical considerations in research design and conduct, researchers can uphold the integrity of their work, maintain trust with participants and the broader community, and contribute to the responsible advancement of knowledge in their field.

Causal Research Data Analysis

Once data is collected, it must be analyzed to draw meaningful conclusions and assess causal relationships.

Causal Inference Methods

Causal inference methods are statistical techniques used to identify and quantify causal relationships between variables in observational data. While experimental designs provide the most robust evidence for causality, observational studies often require more sophisticated methods to account for confounding factors.

  • Difference-in-Differences (DiD):  DiD compares changes in outcomes before and after an intervention between a treatment group and a control group, controlling for pre-existing trends. It estimates the average treatment effect by differencing the changes in outcomes between the two groups over time.
  • Instrumental Variables (IV):  IV analysis relies on instrumental variables—variables that affect the treatment variable but not the outcome—to estimate causal effects in the presence of endogeneity. IVs should be correlated with the treatment but uncorrelated with the error term in the outcome equation.
  • Regression Discontinuity (RD):  RD designs exploit naturally occurring thresholds or cutoff points to estimate causal effects near the threshold. Participants just above and below the threshold are compared, assuming that they are similar except for their proximity to the threshold.
  • Propensity Score Matching (PSM):  PSM matches individuals or units based on their propensity scores—the likelihood of receiving the treatment—creating comparable groups with similar observed characteristics. Matching reduces selection bias and allows for causal inference in observational studies.

Assessing Causality Strength

Assessing the strength of causality involves determining the magnitude and direction of causal effects between variables. While statistical significance indicates whether an observed relationship is unlikely to occur by chance, it does not necessarily imply a strong or meaningful effect.

  • Effect Size:  Effect size measures the magnitude of the relationship between variables, providing information about the practical significance of the results. Standard effect size measures include Cohen's d for mean differences and odds ratios for categorical outcomes.
  • Confidence Intervals:  Confidence intervals provide a range of values within which the actual effect size is likely to lie with a certain degree of certainty. Narrow confidence intervals indicate greater precision in estimating the true effect size.
  • Practical Significance:  Practical significance considers whether the observed effect is meaningful or relevant in real-world terms. Researchers should interpret results in the context of their field and the implications for stakeholders.

Handling Confounding Variables

Confounding variables are extraneous factors that may distort the observed relationship between the independent and dependent variables, leading to spurious or biased conclusions. Addressing confounding variables is essential for establishing valid causal inferences.

  • Statistical Control:  Statistical control involves including confounding variables as covariates in regression models to partially out their effects on the outcome variable. Controlling for confounders reduces bias and strengthens the validity of causal inferences.
  • Matching:  Matching participants or units based on observed characteristics helps create comparable groups with similar distributions of confounding variables. Matching reduces selection bias and mimics the randomization process in experimental designs.
  • Sensitivity Analysis:  Sensitivity analysis assesses the robustness of study findings to changes in model specifications or assumptions. By varying analytical choices and examining their impact on results, researchers can identify potential sources of bias and evaluate the stability of causal estimates.
  • Subgroup Analysis:  Subgroup analysis explores whether the relationship between variables differs across subgroups defined by specific characteristics. Identifying effect modifiers helps understand the conditions under which causal effects may vary.

By employing rigorous causal inference methods, assessing the strength of causality, and addressing confounding variables, researchers can confidently draw valid conclusions about causal relationships in their studies, advancing scientific knowledge and informing evidence-based decision-making.

Causal Research Examples

Examples play a crucial role in understanding the application of causal research methods and their impact across various domains. Let's explore some detailed examples to illustrate how causal research is conducted and its real-world implications:

Example 1: Software as a Service (SaaS) User Retention Analysis

Suppose a SaaS company wants to understand the factors influencing user retention and engagement with their platform. The company conducts a longitudinal observational study, collecting data on user interactions, feature usage, and demographic information over several months.

  • Design:  The company employs an observational cohort study design, tracking cohorts of users over time to observe changes in retention and engagement metrics. They use analytics tools to collect data on user behavior , such as logins, feature usage, session duration, and customer support interactions.
  • Data Collection:  Data is collected from the company's platform logs, customer relationship management (CRM) system, and user surveys. Key metrics include user churn rates, active user counts, feature adoption rates, and Net Promoter Scores ( NPS ).
  • Analysis:  Using statistical techniques like survival analysis and regression modeling, the company identifies factors associated with user retention, such as feature usage patterns, onboarding experiences, customer support interactions, and subscription plan types.
  • Findings: The analysis reveals that users who engage with specific features early in their lifecycle have higher retention rates, while those who encounter usability issues or lack personalized onboarding experiences are more likely to churn. The company uses these insights to optimize product features, improve onboarding processes, and enhance customer support strategies to increase user retention and satisfaction.

Example 2: Business Impact of Digital Marketing Campaign

Consider a technology startup launching a digital marketing campaign to promote its new product offering. The company conducts an experimental study to evaluate the effectiveness of different marketing channels in driving website traffic, lead generation, and sales conversions.

  • Design:  The company implements an A/B testing design, randomly assigning website visitors to different marketing treatment conditions, such as Google Ads, social media ads, email campaigns, or content marketing efforts. They track user interactions and conversion events using web analytics tools and marketing automation platforms.
  • Data Collection:  Data is collected on website traffic, click-through rates, conversion rates, lead generation, and sales revenue. The company also gathers demographic information and user feedback through surveys and customer interviews to understand the impact of marketing messages and campaign creatives .
  • Analysis:  Utilizing statistical methods like hypothesis testing and multivariate analysis, the company compares key performance metrics across different marketing channels to assess their effectiveness in driving user engagement and conversion outcomes. They calculate return on investment (ROI) metrics to evaluate the cost-effectiveness of each marketing channel.
  • Findings:  The analysis reveals that social media ads outperform other marketing channels in generating website traffic and lead conversions, while email campaigns are more effective in nurturing leads and driving sales conversions. Armed with these insights, the company allocates marketing budgets strategically, focusing on channels that yield the highest ROI and adjusting messaging and targeting strategies to optimize campaign performance.

These examples demonstrate the diverse applications of causal research methods in addressing important questions, informing policy decisions, and improving outcomes in various fields. By carefully designing studies, collecting relevant data, employing appropriate analysis techniques, and interpreting findings rigorously, researchers can generate valuable insights into causal relationships and contribute to positive social change.

How to Interpret Causal Research Results?

Interpreting and reporting research findings is a crucial step in the scientific process, ensuring that results are accurately communicated and understood by stakeholders.

Interpreting Statistical Significance

Statistical significance indicates whether the observed results are unlikely to occur by chance alone, but it does not necessarily imply practical or substantive importance. Interpreting statistical significance involves understanding the meaning of p-values and confidence intervals and considering their implications for the research findings.

  • P-values:  A p-value represents the probability of obtaining the observed results (or more extreme results) if the null hypothesis is true. A p-value below a predetermined threshold (typically 0.05) suggests that the observed results are statistically significant, indicating that the null hypothesis can be rejected in favor of the alternative hypothesis.
  • Confidence Intervals:  Confidence intervals provide a range of values within which the true population parameter is likely to lie with a certain degree of confidence (e.g., 95%). If the confidence interval does not include the null value, it suggests that the observed effect is statistically significant at the specified confidence level.

Interpreting statistical significance requires considering factors such as sample size, effect size, and the practical relevance of the results rather than relying solely on p-values to draw conclusions.

Discussing Practical Significance

While statistical significance indicates whether an effect exists, practical significance evaluates the magnitude and meaningfulness of the effect in real-world terms. Discussing practical significance involves considering the relevance of the results to stakeholders and assessing their impact on decision-making and practice.

  • Effect Size:  Effect size measures the magnitude of the observed effect, providing information about its practical importance. Researchers should interpret effect sizes in the context of their field and the scale of measurement (e.g., small, medium, or large effect sizes).
  • Contextual Relevance:  Consider the implications of the results for stakeholders, policymakers, and practitioners. Are the observed effects meaningful in the context of existing knowledge, theory, or practical applications? How do the findings contribute to addressing real-world problems or informing decision-making?

Discussing practical significance helps contextualize research findings and guide their interpretation and application in practice, beyond statistical significance alone.

Addressing Limitations and Assumptions

No study is without limitations, and researchers should transparently acknowledge and address potential biases, constraints, and uncertainties in their research design and findings.

  • Methodological Limitations:  Identify any limitations in study design, data collection, or analysis that may affect the validity or generalizability of the results. For example, sampling biases, measurement errors, or confounding variables.
  • Assumptions:  Discuss any assumptions made in the research process and their implications for the interpretation of results. Assumptions may relate to statistical models, causal inference methods, or theoretical frameworks underlying the study.
  • Alternative Explanations:  Consider alternative explanations for the observed results and discuss their potential impact on the validity of causal inferences. How robust are the findings to different interpretations or competing hypotheses?

Addressing limitations and assumptions demonstrates transparency and rigor in the research process, allowing readers to critically evaluate the validity and reliability of the findings.

Communicating Findings Clearly

Effectively communicating research findings is essential for disseminating knowledge, informing decision-making, and fostering collaboration and dialogue within the scientific community.

  • Clarity and Accessibility:  Present findings in a clear, concise, and accessible manner, using plain language and avoiding jargon or technical terminology. Organize information logically and use visual aids (e.g., tables, charts, graphs) to enhance understanding.
  • Contextualization:  Provide context for the results by summarizing key findings, highlighting their significance, and relating them to existing literature or theoretical frameworks. Discuss the implications of the findings for theory, practice, and future research directions.
  • Transparency:  Be transparent about the research process, including data collection procedures, analytical methods, and any limitations or uncertainties associated with the findings. Clearly state any conflicts of interest or funding sources that may influence interpretation.

By communicating findings clearly and transparently, researchers can facilitate knowledge exchange, foster trust and credibility, and contribute to evidence-based decision-making.

Causal Research Tips

When conducting causal research, it's essential to approach your study with careful planning, attention to detail, and methodological rigor. Here are some tips to help you navigate the complexities of causal research effectively:

  • Define Clear Research Questions:  Start by clearly defining your research questions and hypotheses. Articulate the causal relationship you aim to investigate and identify the variables involved.
  • Consider Alternative Explanations:  Be mindful of potential confounding variables and alternative explanations for the observed relationships. Take steps to control for confounders and address alternative hypotheses in your analysis.
  • Prioritize Internal Validity:  While external validity is important for generalizability, prioritize internal validity in your study design to ensure that observed effects can be attributed to the manipulation of the independent variable.
  • Use Randomization When Possible:  If feasible, employ randomization in experimental designs to distribute potential confounders evenly across experimental conditions and enhance the validity of causal inferences.
  • Be Transparent About Methods:  Provide detailed descriptions of your research methods, including data collection procedures, analytical techniques, and any assumptions or limitations associated with your study.
  • Utilize Multiple Methods:  Consider using a combination of experimental and observational methods to triangulate findings and strengthen the validity of causal inferences.
  • Be Mindful of Sample Size:  Ensure that your sample size is adequate to detect meaningful effects and minimize the risk of Type I and Type II errors. Conduct power analyses to determine the sample size needed to achieve sufficient statistical power.
  • Validate Measurement Instruments:  Validate your measurement instruments to ensure that they are reliable and valid for assessing the variables of interest in your study. Pilot test your instruments if necessary.
  • Seek Feedback from Peers:  Collaborate with colleagues or seek feedback from peer reviewers to solicit constructive criticism and improve the quality of your research design and analysis.

Conclusion for Causal Research

Mastering causal research empowers researchers to unlock the secrets of cause and effect, shedding light on the intricate relationships between variables in diverse fields. By employing rigorous methods such as experimental designs, causal inference techniques, and careful data analysis, you can uncover causal mechanisms, predict outcomes, and inform evidence-based practices. Through the lens of causal research, complex phenomena become more understandable, and interventions become more effective in addressing societal challenges and driving progress. In a world where understanding the reasons behind events is paramount, causal research serves as a beacon of clarity and insight. Armed with the knowledge and techniques outlined in this guide, you can navigate the complexities of causality with confidence, advancing scientific knowledge, guiding policy decisions, and ultimately making meaningful contributions to our understanding of the world.

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Research-Methodology

Causal Research (Explanatory research)

Causal research, also known as explanatory research is conducted in order to identify the extent and nature of cause-and-effect relationships. Causal research can be conducted in order to assess impacts of specific changes on existing norms, various processes etc.

Causal studies focus on an analysis of a situation or a specific problem to explain the patterns of relationships between variables. Experiments  are the most popular primary data collection methods in studies with causal research design.

The presence of cause cause-and-effect relationships can be confirmed only if specific causal evidence exists. Causal evidence has three important components:

1. Temporal sequence . The cause must occur before the effect. For example, it would not be appropriate to credit the increase in sales to rebranding efforts if the increase had started before the rebranding.

2. Concomitant variation . The variation must be systematic between the two variables. For example, if a company doesn’t change its employee training and development practices, then changes in customer satisfaction cannot be caused by employee training and development.

3. Nonspurious association . Any covarioaton between a cause and an effect must be true and not simply due to other variable. In other words, there should be no a ‘third’ factor that relates to both, cause, as well as, effect.

The table below compares the main characteristics of causal research to exploratory and descriptive research designs: [1]

Main characteristics of research designs

 Examples of Causal Research (Explanatory Research)

The following are examples of research objectives for causal research design:

  • To assess the impacts of foreign direct investment on the levels of economic growth in Taiwan
  • To analyse the effects of re-branding initiatives on the levels of customer loyalty
  • To identify the nature of impact of work process re-engineering on the levels of employee motivation

Advantages of Causal Research (Explanatory Research)

  • Causal studies may play an instrumental role in terms of identifying reasons behind a wide range of processes, as well as, assessing the impacts of changes on existing norms, processes etc.
  • Causal studies usually offer the advantages of replication if necessity arises
  • This type of studies are associated with greater levels of internal validity due to systematic selection of subjects

Disadvantages of Causal Research (Explanatory Research)

  • Coincidences in events may be perceived as cause-and-effect relationships. For example, Punxatawney Phil was able to forecast the duration of winter for five consecutive years, nevertheless, it is just a rodent without intellect and forecasting powers, i.e. it was a coincidence.
  • It can be difficult to reach appropriate conclusions on the basis of causal research findings. This is due to the impact of a wide range of factors and variables in social environment. In other words, while casualty can be inferred, it cannot be proved with a high level of certainty.
  • It certain cases, while correlation between two variables can be effectively established; identifying which variable is a cause and which one is the impact can be a difficult task to accomplish.

My e-book,  The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step assistance  contains discussions of theory and application of research designs. The e-book also explains all stages of the  research process  starting from the  selection of the research area  to writing personal reflection. Important elements of dissertations such as  research philosophy ,  research approach ,  methods of data collection ,  data analysis  and  sampling  are explained in this e-book in simple words.

John Dudovskiy

Causal Research (Explanatory research)

[1] Source: Zikmund, W.G., Babin, J., Carr, J. & Griffin, M. (2012) “Business Research Methods: with Qualtrics Printed Access Card” Cengage Learning

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Case study research and causal inference

Judith green.

1 Wellcome Centre for Cultures & Environments of Health, University of Exeter, Exeter, UK

Benjamin Hanckel

2 Institute for Culture and Society, Western Sydney University, Sydney, Australia

Mark Petticrew

3 Department of Public Health, Environments & Society, London School of Hygiene & Tropical Medicine, London, UK

Sara Paparini

4 Wolfson Institute of Population Health, Queen Mary University of London, London, UK

5 Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK

Associated Data

Not applicable; no new data generated in this study.

For the purpose of open access, the author has applied a ‘Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising.

Case study methodology is widely used in health research, but has had a marginal role in evaluative studies, given it is often assumed that case studies offer little for making causal inferences. We undertook a narrative review of examples of case study research from public health and health services evaluations, with a focus on interventions addressing health inequalities. We identified five types of contribution these case studies made to evidence for causal relationships. These contributions relate to: (1) evidence about system actors’ own theories of causality; (2) demonstrative examples of causal relationships; (3) evidence about causal mechanisms; (4) evidence about the conditions under which causal mechanisms operate; and (5) inference about causality in complex systems. Case studies can and do contribute to understanding causal relationships. More transparency in the reporting of case studies would enhance their discoverability, and aid the development of a robust and pluralistic evidence base for public health and health services interventions. To strengthen the contribution that case studies make to that evidence base, researchers could: draw on wider methods from the political and social sciences, in particular on methods for robust analysis; carefully consider what population their case is a case ‘of’; and explicate the rationale used for making causal inferences.

Case study research is widely used in studies of context in public health and health services research to make sense of implementation and service delivery as enacted across complex systems. A recent meta-narrative review identified four broad, overlapping traditions in this body of work: developing and testing complex interventions; analysing change in organisations; undertaking realist evaluations; and studying complex change naturalistically [ 1 ]. Case studies can provide essential thick description of interventions, context and systems; qualitative understanding of the mechanisms of interventions; and evidence of how interventions are adapted in the ‘real’ world [ 2 , 3 ].

However, in evaluative health research, case study designs remain relegated to a minor, supporting role [ 4 , 5 ], typically at the bottom of evidence hierarchies. This relegation is largely due to assumptions that they offer little for making the kinds of causal claims that are essential to evaluating the effects of interventions. The strengths of deep, thick studies of specific cases are conventionally set against the benefits of ‘variable-based’ designs, with the former positioned as descriptive, exploratory or illustrative, and the latter as providing the strongest evidence for making causal claims about the links between interventions and outcomes. In conventional hierarchies of evidence, the primary evidence for making causal claims comes from randomised controlled trials (RCTs), in which the linear relationship between a change in one phenomenon and a later change in another can be delineated from other causal factors. The classic account of causality drawn on in epidemiology requires identifying that the relationship between two phenomena is characterised by co-variation; time order; a plausible relationship; and a lack of competing explanations [ 6 ]. The theoretical and pragmatic limitations of RCT designs for robust and generalizable evaluation of interventions in complex systems are now well-rehearsed [ 2 , 7 – 10 ]. In theory, though, random selection from a population to intervention exposure maximises ability to make causal claims: randomisation minimises risks of confounding, and enables both an unbiased estimate of the effect size of the intervention and extrapolation to the larger population [ 6 ]. Guidance for evaluations in which the intervention cannot be manipulated, such as in natural experiments, therefore typically focuses on methods for addressing threats to validity from non-random allocation in order to strengthen the credibility of probabilistic causal effect estimates [ 4 , 11 ].

This is, however, not the only kind of causal logic. Case study research typically draws on other logics for understanding causation and making causal inferences. We illustrate some of the contributions made by case studies, drawing on a narrative review of research relating to one particularly enduring and complex problem: inequalities in health. The causal chains linking interventions to equity outcomes are long and complex, with recognised limitations in the evidence base for ‘what works’ [ 12 ]. Case study research, we argue, has a critical role to play in making claims about whether, how and why interventions reduce, mitigate, or exacerbate inequalities. Our examples are drawn from a broader review of case study research [ 1 ] and supporting literature reviews [ 5 ], from which we focused on cases which had an explanatory aim, and which shed light on how interventions in public health or health services might reduce, create or sustain inequality. In this paper, we: i) outline some different kinds of evidence relevant to causal relationships that can be  derived from case study research; ii) outline what is needed for case study research to contribute to explanatory, as well as exploratory claims; and iii) advocate for greater clarity in reporting case study research to foster discoverability.

Cases and causes

There are considerable challenges in defining case study designs or approaches in ways that adequately delineate them from other research designs. Yin [ 13 ], for instance, one of the most highly cited source texts on case studies in health research [ 1 ], resists providing a definition, instead suggesting case study research is more a strategy for doing empirical research. Gerring [ 14 ] defines case study research as: “ an intensive study of a single unit for the purpose of understanding a larger class of (similar) units ” (p342, emphasis in original). This definition is useful in suggesting the basis for the inferences drawn from cases, and the need to consider the relationships between the ‘case’ (and phenomena observed within it) and the population from which it is drawn. Gerring notes that studies of single cases may have a greater “affinity” for descriptive aims, but that they can furnish “evidence for causal propositions” ( [ 14 ], p347). Case studies are, he suggests, more likely to be useful in elucidating deterministic causes: those conditions that are necessary and/or sufficient for an outcome, whereas variable based designs have advantages for demonstrating probabilistic causation, where the aim is to estimate the likelihood of two phenomena being causally related. Case studies provide evidence for the mechanisms of causal relationships (e.g. through process tracing, through observing two variables interacting in the real world) and corroboration of causal relationships (for instance, through pattern matching).

Gerring’s argument, drawing on political science examples, is that there is nothing epistemologically distinct about research using the case study: rather, it has particular affinities with certain styles of causal modelling. We take this as a point of departure to consider not whether case studies can furnish evidence to help with causal inference in health research, but rather how they have done this. From our examples on case study research on inequalities in health, we identify the kinds of claims that relate to causality that were made. We note that some relate to (1) Actors’ accounts of causality : that is, the theories of those studied about if, how and why interventions work. Other types of claim use various kinds of comparative analytic logic to elucidate evidence of causal relationships between phenomena. These claims include: (2) Demonstrations of causal relationships – in which evidence from one case is sufficient for identifying a plausible causal relationship; (3) Mechanisms – evidence of the mechanisms through which causal relationships work; (4) Conditions —evidence of the conditions under which such mechanisms operate; and (5) Complex causality —evidence for outcomes that arise from complex causality within a system. This list is neither mutually exclusive, nor exhaustive: many case studies aim to do several of these (and some more). It is also a pragmatic rather than theoretical list, focusing on the kinds of evidence claimed by researchers rather than the formal methodological underpinnings of causal claims (for a discussion of the latter, see Rohlfing [ 15 ]).

What kinds of causal evidence do case studies provide?

Actors’ accounts of causality.

This is perhaps the most common kind of evidence provided by case study research. Case studies, through in-depth research on the actors within systems, can generate evidence about how those actors themselves account for causal relationships between interventions and outcomes. This is an overt aim of many realist evaluation studies, which focus on real forces or processes that exist in the world that can provide insight into causal mechanisms for change.

Ford and colleagues [ 16 ], for example, used a series of five case studies of local health systems to explore socio-economic inequalities in unplanned hospital admission. Cases were selected on the basis of either narrowing or widening inequalities in admission, with a realist evaluation focused on delineating the context-mechanisms-outcome (CMO) configurations in each setting, to develop a broader theory of change for addressing inequalities. The case study approach used a mix of methods, including drawing on documentary data to assess the credibility of mechanisms proposed by health providers. The authors identified 17 distinct CMO configurations; and five factors that were related to trends for inequalities in emergency admissions, including health service factors (primary care workforce challenges, case finding and proactive case management) and those external to the health service (e.g., financial constraints on public services, residential gentrification). Ford and colleagues noted that none of the CMO configurations were clearly associated with improved or worsening trends in inequalities in admission.

Clearly, actors’ accounts of causality are not in themselves evidence of causality. Ford and colleagues noted that they interrogated accounts for plausibility (e.g. that interventions mentioned were prior to effects claimed) and triangulated these accounts with other sources of data, but that inability to empirically corroborate the hypothesized CMO links limited their ability to make claims about causal inference. This is crucial: actors in a system may be aware of the forces and processes shaping change but unaware of counterfactuals, and they are unlikely to have any privileged insight into whether factors are causal or simply co-occurring (see, for instance, Milton et. al. [ 17 ] on how commonly cited ‘barriers’ in accounts of not doing evaluations are also evident in actors’ accounts of doing successful evaluations). Over-interpretation of qualitative accounts of insiders’ claims about causal relationships as if they provide conclusive evidence of causal relationships is poor methodology.

This does not mean that actors’ accounts are not of value. First, in realist evaluation, as in Ford and colleagues’ study [ 16 ], these accounts provide the initial theories of change for thinking about the potential causal pathways in logic models of interventions. Second, insiders’ accounts of causality are part of the system that is being explained. An example comes from Mead and colleagues [ 18 ], who used a case study drawing largely on qualitative interviews to explore “how local actors from public health, and the wider workforce, make sense of and work on social inequalities in health” ( [ 18 ] p168). This used a case study of a partnership in northwest England to address an enduring challenge in inequalities policy: the tendency for policies that address upstream health determinants to transform, in practice, to focus more on behavioural and individual level factors . Local public health actors in the partnership recognised the structural causes of unequal health outcomes, yet discourses of policy action tended to focus only on the downstream, more individualising levels of health, and on personal choice and agency as targets for intervention. Professionals conceptualised action on inequality as relating only to the health of the poorest, rather than as a problem of a gradient in health outcomes across society. There was a geographical localism in their approach, which framed particular places as constellations of health and social problems. Drawing on theory from figurational sociology, Mead and colleagues note that actors’ own accounts are the starting point of an analysis, which then puts those accounts into play with theory about how such discourses are reproduced. The researchers suggest that partnership working itself exacerbated the individualising frameworks used to orient action, as it became a hegemonic framing, reducing the possibilities for partnerships to transform health inequalities. Here, then, a case study approach is used to shed light on the causes of a common failure in policies addressing inequalities. The authors take seriously the divergence of actors’ own accounts of causality and those of other sources, and analyse these as part of the system.

Finally, insider accounts should be taken seriously as contributing to evidence about causal inference through shedding light on the complex looping effects of theoretical models of causality and public accounts. For instance, Smith and Anderson [ 19 ], drawing on a meta-ethnographic literature review of ‘lay theorising’ about health inequalities, note that, counter to common assumptions, public understanding of the structural causes of health inequalities is sophisticated: but that it may be disavowed to avoid stigma and shame and to reassert some agency. This is an important finding for informing knowledge exchange, suggesting that further ‘awareness raising’ may be unnecessary for policy change, and counter-productive in needlessly increasing stigma and shame.

Demonstrations of causal relationships

When strategically sampled, and rooted in a sound theoretical framework, studies of single cases can provide evidence for generalizable causal inferences. The strongest examples are perhaps those that operate as ‘black swans’ for deterministic claims, in that one case may be all that is needed to show that a commonly held assumption is not generalizable. That is, a case study can demonstrate unequivocally that one phenomenon is not inevitably related to another. These can come from cases sampled because they are extreme or unusual. Prior’s [ 20 ] study of a single man in a psychiatric institution in Northern Ireland, for instance, showed that, counter to Goffman’s [ 21 ] original theory of how ‘total institutions’ lead to stigmatisation and depersonalisation, the effects of institutionalisation depended on context—in this case, how the institution related to the local community and the availability of alternative sources of self-worth available to residents.

Strategically sampled typical cases can also provide demonstrative evidence of causal relationships. To take the enduring health services challenge of inequalities in self-referral to emergency care, Hudgins and Rising’s [ 22 ] case study of a single patient is used to debunk a common assumption that high use of emergency care is related to inappropriate care-seeking by low-income patients. They look in detail at the case of “a 51-year-old low-income, recently insured, African American man in Philadelphia (USA) who had two recent ED [emergency department] visits for evaluation of frequent headaches and described fear of being at risk for a stroke.” ( [ 22 ] p50). Drawing on theories of structural violence and patient subjectivity, they use this single case to shed light on why emergency department use may appear inappropriate to providers. They analyse the interplay of gender roles, employment, and insurance status in generating competing drivers of health seeking, and point to the ways in which current policies deterring self-referral do not align well with micro- and macro-level determinants of service use. The study authors also note that because their methods generate data on ‘why’ as well ‘what’ people do, they can “lay the groundwork” ( [ 22 ], p54] for developing future interventions. Here, again, a single case is sufficient. In understanding the causal pathways that led to this patient’s use of emergency care, it is clear why policies addressing inequalities through deterring low-income users would be unlikely to work.

Mechanisms: how causal relationships operate

A strength of case study approaches compared with variable-based designs is furnishing evidence of how causal relationships operate, deriving from both direct observations of causal processes and from analysis of comparisons within and between cases. All cases contain multiple observations; variations can be observed over time and space, across or within cases [ 14 ]. Observing regularities, co-variation and deviant or surprising findings, and then using processes of analytic induction [ 23 ] or abductive logic [ 24 ] to derive, develop and test causal theories using observations from the case, can build a picture of causal pathways.

Process tracing is one formal qualitative methodology for doing this. Widely used in political and policy studies, but less in health evaluations [ 25 ], process tracing links outcomes with their causes, focusing on the mechanisms that link events on causal pathways, and on the strength of evidence for making connections on that causal chain. This requires sound theoretical knowledge (such that credible hypotheses can be developed), well described cases (ideally at different time points), observed causal processes (the activities that transfer causes to effects), and careful assessment of evidence against tests of varying strength for the necessity and sufficiency for accepting or rejecting a candidate hypothesis [ 26 , 27 ]. In health policy, process tracing methods have been combined to good effect with quantitative measures to examine casual processes leading to outcomes of interest. Campbell et. al. [ 28 ], for instance, used process tracing to look at four case studies of countries that had made progress towards universal health coverage (measured through routine data on maternal and neonatal health indicators), to identify key causal factors related to health care workforce.

An example of the use of process tracing in evaluation comes from Lohmann and colleagues’ [ 25 ] case study of a single country, Burkina Faso, to examine why performance based financing (PBF) fails to improve equity. PBF, coupled with interventions to improve health care take up among the poor, aims to improve health equity in low and middle-income countries, yet impact evaluations suggest that these benefits are typically not realised. This case study drew on data from the quantitative impact assessment; programme documentation; the intervention process evaluation; and primary qualitative research for the process tracing, in the light of the theory of change of the intervention. Lohmann and colleagues [ 25 ] identified that a number of conditions that would have been necessary for the intervention to work had not been met (such as eligible patients not receiving the card needed to access health care or providers not receiving timely reimbursement). A key finding was that although implementation challenges were a partial cause of policy failure, other causal conditions were external to the intervention, such as lack of attention to the non-health care costs incurred by the poorest to access care. Again, a single case, if there are good grounds for extrapolating to similar contexts (i.e., those in which transport is required to access health care), is enough to demonstrate a necessary part of the causal pathway between PBF and intended equity outcomes.

Conditions under which causal mechanisms operate

The example of ‘transport access’ as a necessary condition for PBF interventions to ‘work’ also illustrates a fourth type of causal evidence: that relating to the transferability of interventions. Transferable causal claims are essential for useful evidence: “(f)or policy and practice we do not need to know ‘it works somewhere’. We need evidence for ‘it-will-work-for-us’ claims: the treatment will produce the desired outcome in our situation as implemented there” ( [ 8 ] p1401). Some causal mechanisms operate widely (using a parachute will reduce injury from jumping from a plane; taking aspirin will relieve pain); others less so. In the context of health services and public health research, few interventions are likely to be widely generalizable, as the mechanisms will operate differently across contexts [ 7 ]. This context dependency is at the heart of realist evaluations, with the assumption that underlying causal mechanisms require particular contexts in order to operate, hence the focus on ‘how, where, and for whom’ interventions work [ 29 ]. Making useful claims therefore requires other kinds of evidence, relating to what Cartwright and Munro [ 30 ] call the ‘capacities’ of the intervention: what power it has to work reliably, what stops it working, what other conditions are needed for it to work. This evidence is critical for assessing whether an intervention is likely to work in a given context and to assess the intended and unintended consequences of intervention adoption and implementation. Cartwright and Munro’s recommendation is therefore to study causal powers rather than causes. That is, as well as interrogating whether the intervention ‘causes’ a particular outcome, it is also necessary to address the potential for and stability of that causal effect. To do that entails addressing a broader range of questions about the causal relationship, such as how the intervention operates in order to bring about changes in outcomes; what other conditions need to be present; what might constrain this effect; what other factors within the system also promote or constrain those effects; and what happens when different capacities interact? [ 30 ]. Case study research can be vital in providing this kind of evidence on the capacities of interventions [ 31 ].

One example is from Gibson and colleagues [ 32 ], who use within-case comparisons to shed light on why a ‘social prescribing’ intervention may have different effects across socioeconomic classes. These interventions, typically entailing link workers who connect people with complex health care needs to local services and resources, are often framed as a way to address enduring health inequalities. Drawing on sociological theory on how social class is reproduced through socially structured and unequal distribution of resources (‘capitals’), and through how these shape people’s practices and dispositions, Gibson and colleagues [ 32 ] explicate how capitals and dispositions shaped encounters with the intervention. Their analysis of similarities and differences within their case (of different clients) in the context of theory enables them to abstract inferences from the case. Drawing out the ways in which more advantaged clients mobilised capital in their pursuit of health, with dispositions more closely aligned to the intervention, they unravel classed differences in ability to benefit from the intervention, with less advantaged clients inevitably having ‘shorter horizons’ focused on day to day challenges: “This challenges the claim that social prescribing can reduce inequalities, instead suggesting it has the potential to exacerbate existing inequalities” ( [ 32 ], p6).

Case studies can shed light on the capacities of interventions to improve or exacerbate inequalities, including identifying unforeseen consequences. Hanckel and colleagues [ 33 , 34 ], for example, used a case study approach to explore implementation of a physical health intervention involving whole classes of children running for 15 min each day in the playground in schools in south London, UK. This documented considerable adaption of the intervention at the level of school, class and pupil, and identified different pathways through which the intervention might impact on inequalities. In terms of access, the intervention appeared to be equitable, in that there was no evidence of disproportionate roll out to schools with more affluent pupils or to those with fewer minority ethnic pupils [ 33 ]. However, identifying the ‘capacities’ of the intervention also identified other pathways through which it could have negative equity effects. The authors found that in practice, the intervention emphasised body weight rather than physical activity, and intervention roll-out reinforced class and ethnicity-based stigmatising discourses about lower income neighbourhoods [ 34 ].

Complex causality

There is increasing recognition that the systems that reproduce unequal health outcomes are complex: that is, that they consist of multiple interacting components that cannot be studied in isolation, and that change is likely to be non-linear, characterised by, for instance, phase shifts or feedback loops [ 35 ]. This has two rather different implications. First, case study designs can be particularly beneficial for taking a system perspective on interventions. Case studies enable a focus on aspects that are not well explicated through other designs, such as how context interacts with interventions within systems [ 7 ], or on how multiple conditional pathways might link interventions and outcomes [ 36 ]. Second, when causation is not linear, but ‘emergent’, in that it is not reducible to the accumulated changes at lower levels, evaluation designs focused on only one outcome at one level (such as weight loss in individuals) may fail to identify important effects. Case studies have an invaluable role here in unpacking and surfacing these effects at different levels within the systems within which interventions and services are delivered. One example is transport systems, which have been the focus of considerable public health interest to encourage more ‘active’ modes, in which more of the population walk or cycle, and fewer drive. However, more simplistic evaluations looking at one part of a causal chain (such as that between traffic calming interventions and local mode shift) may fail to appreciate how systems are dynamic, and that causation might be emergent. This is evident in a case study of transport policy impacts from Sheller [ 37 ], who takes the case of Philadelphia, USA, to reveal how this post-car trend has racialized effects that can exacerbate inequality. Weaving in data from participant observations, historical documentary sources and statistical evidence of declining car use, Sheller documents the racialized impacts of transport policies which may have reduced car use and encouraged active modes overall, but which have largely prioritised ‘young white’ mobility in the context of local gentrification and neglect of public transit.

One approach to synthesising evidence from multiple case studies to make claims about complex causation is Qualitative Comparative Analysis (QCA), which combines quantitative methods (based on Boolean algebra) with detailed qualitative understanding of a small to medium N sample of cases. This has strengths for identifying multiple pathways to outcomes, asymmetrical sets of conditions which lead to success or failure, or ‘conjunctural causation’, whereby some conditions are only causally linked to outcomes in relation to others [ 38 ]. There is growing interest in using these approaches in evaluative health studies [ 39 ]. One example relating to the effectiveness of interventions addressing inequalities in health comes from Blackman and colleagues [ 36 ], who explored configurations of conditions which did or did not lead to narrowing inequalities in teenage conception rates across a series of local areas as cases. This identified some surprising findings, including that ‘basic’ rather than good or exemplary standards of commissioning were associated with narrowing the equity gap, and that the proportion of minority ethnic people in the population was a key condition.

Not all case study research aims to contribute to causal inference, and neither should it [ 1 , 5 , 40 ]. However, it can. We have identified five ways in which case study evidence has contributed to causal explanations in relation to a particularly intractable challenge: inequalities in health. It is therefore time to stop claiming that case study designs have only a supporting role to play in evaluative health research. To develop a theoretical evidence base on ‘what works’, and how, in health services and public health, particularly around complex issues such as addressing unequal health outcomes, we need to draw on a greater range of evidential resources for informing decisions than is currently used. Best explanations are unlikely to be made from single studies based on one kind of causality, but instead will demand some kind of evidential pluralism [ 41 ]. That is, one single study, of any design, is unlikely to generate evidence for all links in complex causal chains between an intervention and health outcomes. We need a bricolage of evidence from a diverse range of designs [ 42 ] to make robust and credible cases for what will improve health and health equity. This will include evidence from case studies, both from single and small N studies, and from syntheses of findings from multiple cases.

Our focus on case studies that shed light on interventions for health inequalities identified the critical role that case studies can play in theorising, illuminating and making sense of: system actors’ own causal reasoning; whether there are causal links between intervention and outcome; what mechanism(s) might link them; when, where and for whom these causal relationships operate; and how unequal outcomes can be generated from the operation of complex systems. These examples draw on a range of different theoretical and methodological approaches, often from the wider political and social sciences. The approaches illustrated are rooted in very different, even incompatible, philosophical traditions: what researchers understand by ‘causality’ is diverse [ 43 ]. However, there are two commonalities across this diversity that suggest some conditions for producing good case studies that can generate evidence to support causal inferences. The first is the need for theoretically informed and comparative analysis. As Gerring [ 14 ] notes, causal inferences rely on comparisons – across units or time within a case, or between cases. It is comparison that drives the ability to make claims about the potential of interventions to produce change in outcomes of interest, and under what conditions. There are a range of approaches to qualitative data analysis, and choice of method has to be appropriate for the kinds of causal logics being explicated, and the availability of data on particular phenomena within the case. Typically, though, this will require analysis that goes beyond descriptive thematic analysis [ 31 ]. Approaches such as process tracing or analytic induction require both fine-grained and rigorous comparative analysis, and a sound theoretical underpinning that provides a framework for making credible inferences about the relationships between phenomena within the case and to the wider population from which the case is selected.

This leads to the second commonality: the need to clarify what the case is a case ‘of’, and how it relates to other candidate cases. What constitutes a ‘case’ is inevitably study specific. The examples we have drawn on include: PBF in a country [ 25 ], transport systems in a city [ 37 ], and a social prescribing intervention in primary care [ 32 ]. Clearly, in other contexts, each of these ‘cases’ could be sampling units within variable based studies (of financing systems, or countries; of infrastructures systems, or cities in a state; of particular kinds of service intervention, or primary care systems). Conversely, these cases could be populations within which lower level phenomena (districts, neighbourhoods, patients) are studied. What leads to appropriate generalisations about causal claims is a sound theorisation of the similarities and particularities of the case compared with other candidate cases: how Burkina Faso has commonalities with, or differences from, other settings in which PBF has failed to improve equity; or the contexts of gentrification and residential churn that make Philadelphia similar to other cities in the US; or the ways in which class-based dispositions and practices intersect with similar types of service provisions.

A critical question remains: How can well-conducted case study evidence be better integrated into the evidence base? Calls for greater recognition for case study designs within health research are hardly new: Flyvberg’s advocacy for a greater role for case studies in the social sciences [ 44 ] has now been cited around 20,000 times, and calls for methodological pluralism in health research go back decades [ 42 , 45 , 46 ]. Yet, case studies remain somewhat neglected, with ongoing misconceptions about their limited role, despite calls for evidence based medicine to incorporate evidence for mechanisms as complementary to evidence of correlation, rather than as inferior [ 47 ]. Even where the value of case studies for contributing to causal inference is recognised, searching for good evidence is not straightforward. Case studies are neither consistently defined nor necessarily well reported. Some of the examples in this paper do not use the term ‘case study’ in the title or abstract, although they meet our definition. Conversely, many small scale qualitative studies describe themselves as ‘case studies’, but focus on thick description rather than generalisability, and are not aiming to contribute to evaluative evidence. It is therefore challenging, currently, to undertake a more systematic review of empirical material. Forthcoming guidance on reporting case studies of context in complex systems aims to aid discoverability and transparency of reporting (Shaw S, et al: TRIPLE C Reporting Principles for Case study evaluations of the role of Context in Complex interventions, under review). This recommends including ‘case study’ in the title, clarifying how terms are used, and explicating the philosophical base of the study. To further advance the usefulness of case study evidence, we suggest that where an aim is to contribute to causal explanations, researchers should, in addition, specify their rationales for making causal inferences, and identify what broader class of phenomena their case is a case ‘of’.

Conclusions

Case study research can and does contribute to evidence for causal inferences. On challenging issues such as addressing health inequalities, we have shown how case studies provide more than detailed description of context or process. Contributions include: describing actors’ accounts of causal relationships; demonstrating theoretically plausible causal relationships; identifying mechanisms which link cause and effect; identifying the conditions under which causal relationships hold; and researching complex causation.

Acknowledgements

The research underpinning this paper was conducted as part of the Triple C study. We gratefully acknowledge the input of the wider study team, and that of the participants at a workshop held to discuss forthcoming guidance on reporting case study research.

Abbreviations

Authors’ contributions.

BH, JG and MP drafted the first version of the paper, which was revised with theoretical input from SS and SP. All authors contributed to the paper and have reviewed and approved the final manuscript.

The research was funded by the Medical Research Council (MR/S014632/1). JG is supported with funding from the Wellcome Trust (WT203109/Z/16/Z). Additional funding for SP and SS salaries over the course of the study was provided by the UK National Institute for Health Research Oxford Biomedical Research Centre (BRC-1215–20008), Wellcome Trust (WT104830MA; 221457/Z/20/Z) and the University of Oxford's Higher Education Innovation Fund.

The views and opinions expressed herein are those of the authors. Funding bodies had no input to the design of the study and collection, analysis, and interpretation of data or preparation of this paper.

Availability of data and materials

Declarations.

Not applicable.

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

  • What is Causal Research? Definition + Key Elements

Moradeke Owa

Cause-and-effect relationships happen in all aspects of life, from business to medicine, to marketing, to education, and so much more. They are the invisible threads that connect both our actions and inactions to their outcomes. 

Causal research is the type of research that investigates cause-and-effect relationships. It is more comprehensive than descriptive research, which just talks about how things affect each other.

Let’s take a closer look at how you can use informal research to gain insight into your research results and make more informed decisions.

What’s the Difference Between Correlation and Causation

Defining Causal Research

Causal research investigates why one variable (the independent variable) is causing things to change in another ( the dependent variable). 

For example, a causal research study about the cause-and-effect relationship between smoking and the prevalence of lung cancer. Smoking prevalence would be the independent variable, while lung cancer prevalence would be the dependent variable. 

You would establish that smoking causes lung cancer by modulating the independent variable (smoking) and observing the effects on the dependent variable (lung cancer).

What’s the Difference Between Correlation and Causation

Correlation simply means that two variables are related to each other. But it does not necessarily mean that one variable causes changes in the other. 

For example, let’s say there is a correlation between high coffee sales and low ice cream sales. This does not mean that people are not buying ice cream because they prefer coffee. 

Both of these variables correlate because they’re influenced by the same factor: cold weather.

The Need for Causal Research

Examples of Where Causal Relationships Are Critical

The major reason for investigating causal relationships between variables is better decision-making , which leads to developing effective solutions to complex problems. Here’s a breakdown of how it works:

  • Decision-Making

Causal research enables us to figure out how variables relate to each other and how a change in one variable affects another. This helps us make better decisions about resource allocation, problem-solving, and achieving our goals.

In business, for example, customer satisfaction (independent variable) directly impacts sales (dependent variable). If customers are happy with your product or service, they’re more likely to keep returning and recommending it to their friends, which translates into more sales.

  • Developing Effective Solutions to Problems

Understanding the causes of a problem,  allows you to develop more effective solutions to address it. For example, medical causal research enables you to understand symptoms better, create new prevention strategies, and provide more effective treatment for illnesses.

Key Elements of Causal Research

Examples of Where Causal Relationships Are Critical

Here are a couple of ways  you can leverage causal research:

  • Policy-making : Causal research informs policy decisions about issues such as education, healthcare, and the environment. Let’s say causal research shows that the availability of junk food in schools directly impacts the prevalence of obesity in teenagers. This would inform the decision to incorporate more healthy food options in schools.
  • Marketing strategies : Causal research studies allow you to identify factors that influence customer behavior to develop effective marketing strategies. For example, you can use causal research to reach and attract your target audience with the right content.
  • Product development : Causal research enables you to create successful products by understanding users’ pain points and providing products that meet these needs.

Research Designs for Establishing Causality

Key Elements of Causal Research

Let’s take a deep dive into what it takes to design and conduct a causal study:

  • Control and Experimental Groups

In a controlled study, the researchers randomly put people into one of two groups: the control group, who don’t get the treatment, or the experimental group, who do.

Having a control group allows you to compare the effects of the treatment to the effects of no treatment. It enables you to rule out the possibility that any changes in the dependent variable are due to factors other than the treatment.

  • Independent variable : The independent variable is the variable that affects the dependent variable. It is the variable that you alter to see the effect on the dependent variable.
  • Dependent variable : The dependent variable is the variable that is affected by the independent variable. This is what you measure to see the impact of the independent variable.

An Illustration of How Independent vs Dependent Variable Works in Causal Research

Here’s an illustration to help you understand how to differentiate and use variables in causal research:

Let’s say you want to investigate “ the effect of dieting on weight loss ”, dieting would be the independent variable, and weight loss would be the dependent variable. Next, you would vary the independent variable (dieting) by assigning some participants to a restricted diet and others to a control group. 

You will see the cause-and-effect relationship between dieting and weight loss by measuring the dependent variable (weight loss) in both groups.

Skip the setup hassle! Get a head start on your research with our ready-to-use Experimental Research Survey Template

Research Designs for Establishing Causality

There are several ways to investigate the relationship between variables, but here are the most common:

A. Experimental Design

Experimental designs are the gold standard for establishing causality. In an experimental design, the researcher randomly assigns participants to either a control group or an experimental group. The control group does not receive the treatment, while the experimental group does.

Pros of experimental designs :

  • Highly rigorous
  • Explicitly establishes causality
  • Strictly controls for extraneous variables
  • Time-consuming and expensive
  • Difficult to implement in real-world settings
  • Not always ethical

B. Quasi-Experimental Design

A quasi-experimental design attempts to determine the causal relationship without fully randomizing the participant distribution into groups. The primary reason for this is ethical or practical considerations.

Different types of quasi-experimental designs

  • Time series design : This design involves collecting data over time on the same group of participants. You see the cause-and-effect relationship by identifying the changes in the dependent variable that coincide with changes in the independent variable.
  • Nonequivalent control group design : This design involves comparing an experimental group to a control group that is not randomly assigned. The differences between the two groups explain the cause-and-effect relationship.
  • Interrupted time series design : Unlike the time series that measures changes over time, this introduces treatment at a specific point in time. You figure out the relationship between treatment and the dependent variable by looking for any changes that occurred at the time the treatment was introduced.

Pros of quasi-experimental designs

  • Cost-effective
  • More feasible to implement in real-world settings
  • More ethical than experimental designs
  • Not as thorough as experimental designs
  • May not accurately establish causality
  • More susceptible to bias

Establishing Causality without Experiments

Using experiments to determine the cause-and-effect relationship between each dependent variable and the independent variable can be time-consuming and expensive. As a result, the following are cost-effective methods for establishing a causal relationship:

  • Longitudinal Studies

Long-term studies are observational studies that follow the same participants or groups over a long period. This way, you can see changes in variables you’re studying over time, and establish a causal relationship between them.

For example, you can use a longitudinal study to determine the effect of a new education program on student performance. You then track students’ academic performance over the years to see if the program improved student performance.

Challenges of Longitudinal Studies

One of the biggest problems of longitudinal studies is confounding variables. These are factors that are related to both the independent variable and the dependent variable.

Confounding variables can make it hard to isolate the cause of an independent variable’s effect. Using the earlier example, if you’re looking at how new educational programs affect student success, you need to make sure you’re controlling for factors such as students’ socio-economic background and their prior academic performance.

  • Instrumental Variables (IV) Analysis

Instrumental variable analysis (IV) is a statistical approach that enables you to estimate causal effects in observational studies. An instrumental variable is a variable that is correlated with the independent variable but is not correlated with the dependent variable except through the independent variable.

For example, in academic achievement research, an instrumental variable could be the distance to the nearest college. This variable is correlated with family income but doesn’t correlate with academic achievement except through family income.

Challenges of Instrumental Variables (IV) Analysis

A primary limitation of IV analysis is that it can be challenging to find a good instrumental variable. IV analysis can also be very sensitive to the assumptions of the model.

Challenges and Pitfalls

Establishing Causality without Experiments

It is a powerful tool for solving problems, making better decisions, and advancing human knowledge. However, causal research is not without its challenges and pitfalls.

  • Confounding Variables

A confounding variable is a variable that correlates with both the independent and dependent variables, and it can make it difficult to isolate the causal effect of the independent variable. 

For example, let’s say you are interested in the causal effect of smoking on lung cancer. If you simply compare smokers to nonsmokers, you may find that smokers are more likely to get lung cancer. 

However, the relationship between smoking and lung cancer may be confounded by other factors, such as age, socioeconomic status, or exposure to secondhand smoke. These other factors may be responsible for the increased risk of lung cancer in smokers, rather than smoking itself.

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Strategy to Control for Confounding Variables

Confounding variables can lead to misleading results and make it difficult to determine the cause-and-effect between variables. Here are some strategies that allow you to control for confounding variables and improve the reliability of causal research findings:

  • Randomized Controlled Trial (RCT)

In an RCT, participants are randomly assigned to either the treatment group or the control group. This ensures that the two groups are comparable on all confounding variables, except for the treatment itself.

  • Statistical Methods

Using statistical methods such as multivariate regression analysis allows you to control for multiple confounding variables simultaneously.

Reverse Causation

Reverse Causation is when the relationship between the cause and effect of variables is reversed. 

For example, let’s say you want to find a correlation between education and income. You’d expect people with higher levels of education to earn more, right? 

Well, what if it’s the other way around? What if people with higher income are only more college-educated because they can afford it and lower-income people can’t?

Strategy to Control for Reverse Causation

Here are some ways to prevent and mitigate the effect of reverse causation:

  • Longitudinal study

A longitudinal study follows the same individuals or groups over time. This allows researchers to see how changes in one variable (e.g., education) are associated with changes in another variable (e.g., income) over time.

  • Instrumental Variables Analysis

Instrumental variables analysis is a statistical technique that estimates the causal effect of a variable when there is reverse causation.

Real-World Applications

Causal research allows us to identify the root causes of problems and develop solutions that work. Here are some examples of the real-world applications of causal research:

  • Healthcare Research:

Causal research enables healthcare professionals to figure out what causes diseases and how to treat them.

 For example, medical researchers can use casual research to figure out if a drug or treatment is effective for a specific condition. It also helps determine what causes certain diseases.

Randomized controlled trials (RCTs) are widely regarded as the standard for determining causal relationships in healthcare research. They have been used to determine the effects of multiple medical interventions, such as the effectiveness of new drugs and vaccines, surgery, as well as lifestyle changes on health.

  • Public Policy Impact

Causal research can also be used to inform public policy decisions. For example, a causal study showed that early childhood education for disadvantaged children improved their academic performance and reduced their likelihood of dropping out. This has been leveraged to support policies that increase early childhood education access.

You can also use causal research to see if existing policies are working. For example, a causal study proves that giving ex-offenders job training reduces their chances of reoffending. The governments would be motivated to set up, fund, and mandate ex-offenders to take training programs.

Understanding causal effects helps us make informed decisions across different fields such as health, business, lifestyle, public policy, and more. But, this research method has its challenges and limitations.

Using the best practices and strategies in this guide can help you mitigate the limitations of causal research. Start your journey to seamlessly collecting valid data for your research with Formplus .

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  • casual research
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causal analysis

Complete Guide on Causal Analysis Essay Writing

causal research paper example

Don’t worry if you have been given a causal analysis essay to write and have no idea how to start. We have put together an easy to follow guide for you on our essay service to be done as fast as possible!

What is Causal Analysis Essay?

The aim of a causal analysis paper is to show either the consequences of certain causes and effects and vice versa. This is best explored through an essay in which the question " why? " is answered.

causal

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The overall conclusion is usually intended to either prove a point , speculate a theory or disprove a common belief .

This could also be explained through a philosophical narrative by saying it tries to answer the “why” in our lives by clarifying the world in which we inhabit. So, therefore the causal analysis can be said to help us comprehend the complex series of events that shape our life.

To simplify further into an equation this is how you could write it:

Causal analysis essay definition

50 Causal Analysis Essay Topics

The choice of causal analysis essay topics is by far one of the most responsible steps in handling the task because it affects how easy and fast the process goes and how good the result will be.

Depending on your academic level and the subject, the choice of causal argument essay topics can be very extensive.

So, how do you make the right choice?

This may surprise you, but the key to choosing the best causal essay topics is focusing on one’s own interests. When writing on a topic that you are genuinely interested in, the process will not feel as stressful and boring, and the result will be much better than if you’d write on a topic that is too boring or complex to you.

Need some ideas? To help you get on the right track, we prepared a list of 50 great topics for inspiration:

Technology Causal Analysis Essay Topics

  • How can the popularization of e-learning harm the traditional educational system?
  • The effects of too active Internet use on children’s personalities.
  • What are the reasons that make cyberbullying such a major issue in the modern world?
  • How does technology make our day-to-day lives more complicated?
  • The impact of IT industry growth on immigration.
  • The positive impact of technology on the healthcare industry.
  • Influence of technology on attention spans and perception of information.
  • How is technology changing a modern classroom?
  • How has increased internet access influenced children’s and teen’s behavior?
  • What effects does growing misinformation on the internet have on us?

Political Causal Analysis Essay Topics

  • Does social media influence politics in any way today?
  • What causes a growing number of mass shooting cases in the US?
  • Cause-and-effect of the feminist movement.
  • The correlation between success in the political sphere and the chosen style of language.
  • Are there still hints of gender bias in politics?
  • Why do successful political leaders tend to resign at the peak of their careers?
  • What has caused stricter gun policies in the US?
  • The role of the Civil Rights Movement in the US politics.
  • Cause-and-effect of globalization and labor market.
  • What led to the US government shut down in 2013?.

Global Occurrences Causal Analysis Essay Topics

  • Why did Covid-19 have such a negative impact on the global economy?
  • The positive impact the Black Lives Matter movement has on our society.
  • How well did we handle the global pandemic?
  • Why is the Chinese government planning to back away from its one-child policy?
  • What has caused the Israel-Palestine Crisis?
  • Why did Donald Trump become the first US president to be impeached twice?
  • Why do cryptocurrencies have the potential to replace traditional money?
  • Why are people investing in cryptocurrency?
  • Why does Elon Musk consider using Bitcoin again?
  • Why is the gradual border reopening strategy vital for the EU countries?

Education Causal Analysis Essay Topics

  • What causes a consistently high number of bullying cases in schools?
  • The negative impact of bullying at schools.
  • How is children’s emotional development being affected by the educational system?

How well did we handle adaptation to e-learning during the pandemic?

  • What factors make distance learning a bad thing in terms of socializing?
  • Why does school uniform have a positive effect on students’ performance?
  • The perks of the blended learning approach.
  • Why do children tend to perceive new information faster and retain it better than adults?
  • The pros and cons of homework.
  • Why should parents get more involved in school life?

Nature and Environment Causal Analysis Essay Topics

  • What is causing global warming, and what effects might it have on our environment?
  • The negative effects of the increasing water pollution levels on our lives.
  • What factors cause certain species of animals to go extinct?
  • What are the positive effects of owning a pet for children?
  • How do our daily activities affect nature and the environment?
  • The positive effects of various environmental protection programs on wildlife and nature.
  • What makes zoos worse than national parks?
  • Why do scientists use animals for research and studies?
  • Cause-and-effect of environmental pollution.
  • The positive effect of fully organic food and goods on a human health.

Causal Analysis Essay Outline

Plan out an outline to make your writing easier and faster then all the elements of the article will come together better in the end. Also if you want to pay someone to write my essay - EssayService it is a good idea.

Choose a Causal Analysis Essay Topic

To start it is best to decide on a topic you wish to explore and is something that has meaning or is a subject area already known about. Think carefully about the causes and effects that could transpire from a given area or topic and also perhaps something that is controversial and open to discussion. It may not be possible to write fully about both the causes and effects so keep in mind which will be the stronger point to include in the paper.

Write a Causal Analysis Essay Thesis Statement

After the chosen topic is decided it is possible to plan out what the causal analysis will find out by creating the thesis statement. This should be summarized into one or two sentences and focus on a particular subject area that can be explored. Try not to limit the essay too much by including too much detail or using language that prevents exploring further possibilities.

An example of a thesis statement could look like:

Governments around the world are meant to have our best interests at heart, yet why do their policies anger many and cause protests. Is this related to bad choice of politicians and political voting systems used and what other factors can be involved?

Create a Causal Analysis Essay Introduction

It is a good idea to put the thesis at the end of the introduction which should give some basic information on the topic. You should start with a “hook” or opening sentence that will grab the reader's attention and want them to continue reading. An interesting quote or statistic can be a good example or something that will make the reader think about the topic.

Write a Causal Analysis Essay Body Paragraphs

Create every paragraph to illustrate one cause or effect chain and write it logically. Use examples to demonstrate the thinking process and the specific chain of causes or effects. Make sure each chain is set out chronologically to make everything clear to the reader. Always clarify the cause to effect or vice versa relationship instead of making comparisons as this will make your statements stronger.

Write a Causal Analysis Essay Conclusion

At the end of the paper include a concluding paragraph which should be a summary of the connections that have been discovered on significant cause-effect relationship. Remember to finish the paper with something that is thought provoking or memorable that highlights the conclusions within the article. For example, if the paper was about World war II, say due to these causes or effects that a third world war is possible if these factors are not kept in check.

Tips for Writing a Causal Analysis Essay

Unless you decide to buy essays online from our service, you should follow the tips below to make your writing worth the best grade.

causal tips

Keep all the links . Do not leave out any links in the chain of causes and effects unless you are certain that the reader can make the correct connections.

Leave any biases out. It is important to develop an honest essay, to be impartial, and not already have any prejudices. According to our write my essay service professionals, to be a credible writer and make the audience believe in the analysis, the work should be from a neutral stance.

Backup everything with sufficient evidence. Always give specific details and support with hard evidence. Never be vague with the connections in the chain and explain all the links.

Don't oversimplify things. While it is needed to focus and limit the analysis to particular points of the thesis, do not be too quick to assign cause and effect conclusions. Think carefully before making statements and do not jump to any false predictions before evaluating properly.

Try not fall into the post hoc trap. This can be avoided by not making any errors in the logic used and carefully researching each link in the chain. This is a typical causal relationship error that links a previous subject in time just because it happened before. For example, coming to the conclusion that marijuana smokers will go on to smoke crack. This could be based on that crack smokers have tried marijuana before they tried crack but this is a false connection. With the same logic, it could said that cigarette smoking would lead to smoking crack and marijuana, but this is also post hoc fallacy.

Avoid circular thought processes. Try not use thought processes that have no definite conclusion and just restate the thesis. Make new links and ideas that do not end at where the statement started, finish with a sense of conclusion.

Causal Analysis Essay Example

As mentioned above, a causal analysis essay is a form of academic writing task that analyzes the cause of a problem. Some people also refer to causal analysis essays as cause and effect essays.

This type of essay explores the critical aspects of a specific issue to determine the primary causes. You need to state your claim and back it up with supporting facts and arguments. Besides, example essays on causal analysis correlate every issue with an underlying problem.

For instance, most global warming essays are a typical example essay on causal analysis because they highlight factors like human activity (and inactivity) and how it impacts the environment. 

Now let’s check out a sample essay on the following topic: ‍

The global pandemic has presented massive challenges in all aspects of human life. Many individuals have lost their livelihoods, while companies had to digitize their processes to address the financial strains. In schools, the shift to e-learning has also come at an unprecedented pace, forcing teachers and school administrators to adopt new technologies and teaching methods to keep the learning process going. However, the adaptation process to e-learning has not been a major success for students.
Since the start of the pandemic, schools have tried to switch to e-learning and replicate traditional classes online. However, this process has been hindered by unpreparedness in most schools. Due to the unprecedented nature of the pandemic, lecturers did not have enough time to acquaint themselves with modern technological platforms. Consequently, they lacked the technical knowledge to get the best of the available learning tools and platforms.
Furthermore, students seem to enjoy e-learning, but the problem lies in the fact that they cannot harness their academic potential to the fullest. In developing countries, poverty, corruption, and inadequate access to learning infrastructure present a massive obstacle to students. Moreover, students living in countries without stable electricity and internet connection lag behind their peers from other countries. And since most schools cannot change the financial situation of disenfranchised students, these young people get left out of the overall academic cycle. 
In line with the lack of access to essential learning materials, students are losing interest in academics. As a result, the dropout rates in higher institutions have reached record numbers over the past 18 months. Some experts ascribe the increasing dropout rates to poverty and financial instability across the globe (Morin, 2021). However, other experts claim that these dropout rates are directly correlated with the hasty and poor implementation of e-learning in schools across the globe. Students who feel abandoned by the system have no motivation to continue pursuing their degrees. Alternatively, they are exploring other career options to maintain financial stability or support their siblings.
On the other hand, student engagement has remained high throughout the pandemic. Teachers now use advanced communication channels and learning tools to connect with their students during and beyond class hours. Gamification has also become an integral part of learning, as online laboratories and virtual reality tools come to the fore. Moreover, the introduction of exciting digital tools into the curriculum has motivated students to stay engaged in the educational process, thus improving their overall performance across the board. Essentially, the increase in online classroom engagement has also boosted students’ academic performance and their understanding of the curriculum.
In conclusion, the merits of the current iteration of e-learning are few and far between. Schools need to address their e-learning models right away to avoid pushing more students away from the academic system. Students from low-income communities should be encouraged to stay in school by creating subsidies for them and re-integrating them into the academic fold. Ultimately, the entire academia should focus on creating modern technological solutions to bridge the expanding knowledge gap caused by the pandemic.

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Measurement issues in causal inference

  • Published: 11 March 2024

Cite this article

  • Benjamin R. Shear   ORCID: orcid.org/0000-0002-9236-2927 1 &
  • Derek C. Briggs   ORCID: orcid.org/0000-0003-1628-4661 1  

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Research in the social and behavioral sciences relies on a wide range of experimental and quasi-experimental designs to estimate the causal effects of specific programs, policies, and events. In this paper we highlight measurement issues relevant to evaluating the validity of causal estimation and generalization. These issues impact all four categories of threats to validity previously delineated by Shadish et al. (Experimental and quasi-experimental designs for generalized causal inference. Houghton Mifflin, Boston, 2002): internal, external, statistical conclusion, and construct validity. We use the context of estimating the effect of the COVID-19 pandemic on student learning in the U.S. to illustrate the important role of measurement in causal inference. We provide background related to the meaning of measurement, and focus attention on the evidence and argumentation necessary to evaluate the validity and reliability of the different types of measures used in statistical models for causal inference. We conclude with recommendations for researchers estimating and generalizing causal effects: provide clear statements for construct interpretations, seek to rule out potential sources of construct-irrelevant variance, quantify and adjust for measurement error, and consider the extent to which interpretations of practical significance are consistent with scale properties of outcome measures.

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causal research paper example

Source: https://www.nationsreportcard.gov/highlights/ltt/2022/

causal research paper example

Technically this scenario represents what is sometimes referred to as “reflective measurement” in which observed item responses are assumed to reflect variation in the construct of interest. This contrasts with “formative measurement” in which the observed indicators are assumed to define the construct of interest, for example measuring socioeconomic status (SES) based on a set of observed characteristics including income and job status. We focus on reflective measurement models because we believe these better represent the types of latent constructs most widely used in social science research, but note that the choice between reflective and formative measurement models can have important conceptual and statistical implications (Rhemtulla et al., 2020 ).

The Standards use the generic term “test” to refer to measurement procedures generally, noting “A test is a device or procedure in which a sample of an examinee’s behavior in a specified domain is obtained and subsequently evaluated and scored using a standardized process. Whereas the label test is sometimes reserved for instruments on which responses are evaluated for their correctness or quality, and the terms scale and inventory are used for measures of attitudes, interest, and dispositions, the Standards uses the single term test to refer to all such evaluative devices” (2014, p. 2; emphasis original). The issues we discuss are relevant for all measurement procedures falling under this more general definition of tests.

https://www.air.org/project/naep-validity-studies-nvs-panel

This falls under what Truman Kelley described as jingle-jangle fallacies (Kelley, 1927 ), which apply when one assumes that two tests measure the same construct because they share the same name (jingle fallacy) or that two tests measure different constructs because they have different names (jangle fallacy).

We note that because the NAEP tests focus on group-level summaries, the NAEP tests use a combination of item response theory, marginal maximum likelihood estimation, and plausible values to account for the high level of uncertainty in student-level scores due to item sampling (Mislevy et al., 1992 ). Measurement issues associated with complex implementations of item response theory modeling (e.g., adaptive and multistage designs, linking and equating, latent regression, multidimensionality, etc.) goes beyond the scope of the current discussion. See Mislevy ( 1984 ), Oranje and Kolstad ( 2019 ), Skrondal and Rabe-Hesketh ( 2004 ), and van der Linden ( 2016 ). For discussions on the use of NAEP’s plausible values in econometric modeling, see Jacob and Rothstein ( 2016 ) or Braun and Von Davier ( 2017 ).

Note that regression discontinuity designs are a special case in which treatment status is assigned based on an error-prone score and it is assumed that assignment depends on random measurement error, thus making groups near a threshold equivalent.

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Shear, B.R., Briggs, D.C. Measurement issues in causal inference. Asia Pacific Educ. Rev. (2024). https://doi.org/10.1007/s12564-024-09942-9

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Title: a data-driven two-phase multi-split causal ensemble model for time series.

Abstract: Causal inference is a fundamental research topic for discovering the cause-effect relationships in many disciplines. However, not all algorithms are equally well-suited for a given dataset. For instance, some approaches may only be able to identify linear relationships, while others are applicable for non-linearities. Algorithms further vary in their sensitivity to noise and their ability to infer causal information from coupled vs. non-coupled time series. Therefore, different algorithms often generate different causal relationships for the same input. To achieve a more robust causal inference result, this publication proposes a novel data-driven two-phase multi-split causal ensemble model to combine the strengths of different causality base algorithms. In comparison to existing approaches, the proposed ensemble method reduces the influence of noise through a data partitioning scheme in the first phase. To achieve this, the data are initially divided into several partitions and the base algorithms are applied to each partition. Subsequently, Gaussian mixture models are used to identify the causal relationships derived from the different partitions that are likely to be valid. In the second phase, the identified relationships from each base algorithm are then merged based on three combination rules. The proposed ensemble approach is evaluated using multiple metrics, among them a newly developed evaluation index for causal ensemble approaches. We perform experiments using three synthetic datasets with different volumes and complexity, which are specifically designed to test causality detection methods under different circumstances while knowing the ground truth causal relationships. In these experiments, our causality ensemble outperforms each of its base algorithms. In practical applications, the use of the proposed method could hence lead to more robust and reliable causality results.

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The Causal Effect of Parents’ Education on Children’s Earnings

We present a model of endogenous schooling and earnings to isolate the causal effect of parents’ education on children’s education and earnings outcomes. The model suggests that parents’ education is positively related to children’s earnings, but its relationship with children’s education is ambiguous. Identification is achieved by comparing the earnings of children with the same length of schooling, whose parents have different lengths of schooling. The model also features heterogeneous preferences for schooling, and is estimated using HRS data. The empirically observed positive OLS coefficient obtained by regressing children’s schooling on parents’ schooling is mainly accounted for by the correlation between parents’ schooling and children’s unobserved preferences for schooling. This is countered by a negative, structural relationship between parents’ and children’s schooling choices, resulting in an IV coefficient close to zero when exogenously increasing parents’ schooling. Nonetheless, an exogenous one-year increase in parents’ schooling increases children’s lifetime earnings by 1.2 percent on average.

None of the authors have received any funding for this research. We thank Antonio Ciccone, Mariacristina De Nardi, Steven Durlauf, Eric French and Chris Taber for very helpful conversations. The paper also benefited from conference and seminar participants at the 2015 ASSA and EEA meetings, University of Cyprus, Essex, and TSE. Junjie Guo and Wei Song provided outstanding research assistance. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

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Metabolic-dysfunction associated steatotic liver disease-related diseases, cognition and dementia: A two-sample mendelian randomization study

  • Li, Yao-Shuang
  • Liu, Yan-Lan
  • Jiang, Wei-Ran
  • Qiu, Hui-Na
  • Li, Jing-Bo
  • Lin, Jing-Na

Background The results of current studies on metabolic-dysfunction associated steatotic liver disease (MASLD)-related diseases, cognition and dementia are inconsistent. This study aimed to elucidate the effects of MASLD-related diseases on cognition and dementia. Methods By using single-nucleotide polymorphisms (SNPs) associated with different traits of NAFLD (chronically elevated serum alanine aminotransferase levels [cALT], imaging-accessed and biopsy-proven NAFLD), metabolic dysfunction-associated steatohepatitis, and liver fibrosis and cirrhosis, we employed three methods of mendelian randomization (MR) analysis (inverse-variance weighted [IVW], weighted median, and MR-Egger) to determine the causal relationships between MASLD-related diseases and cognition and dementia. We used Cochran's Q test to examine the heterogeneity, and MR-PRESSO was used to identify outliers (NbDistribution = 10000). The horizontal pleiotropy was evaluated using the MR-Egger intercept test. A leave-one-out analysis was used to assess the impact of individual SNP on the overall MR results. We also repeated the MR analysis after excluding SNPs associated with confounding factors. Results The results of MR analysis suggested positive causal associations between MASLD confirmed by liver biopsy (p of IVW = 0.020, OR = 1.660, 95%CI = 1.082–2.546) and liver fibrosis and cirrhosis (p of IVW = 0.009, OR = 1.849, 95%CI = 1.169–2.922) with vascular dementia (VD). However, there was no evidence of a causal link between MASLD-related diseases and cognitive performance and other types of dementia (any dementia, Alzheimer's disease, dementia with lewy bodies, and frontotemporal dementia). Sensitivity tests supported the robustness of the results. Conclusions This two-sample MR analysis suggests that genetically predicted MASLD and liver fibrosis and cirrhosis may increase the VD risk. Nonetheless, the causal effects of NAFLD-related diseases on VD need more in-depth research.

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