Causal Research (Explanatory research)
Causal research, also known as explanatory research, is a type of research that aims to identify and explain cause-and-effect relationships between variables
On this page:
- Meaning of causal research and its components
- Causal research methods
- Examples of causal research methods
- Advantages and disadvantages
- Causal research in the age of AI and digital business
- When to use causal research
| Feature | Causal Research | Exploratory Research | Descriptive Research |
|---|---|---|---|
| Main purpose | Explain cause-and-effect relationships | Explore unclear problems | Describe characteristics or situations |
| Main research statement | Hypothesis | Research question | Research question |
| Level of structure | Highly structured | Flexible and unstructured | Structured |
| Typical stage | Later stages of research | Early stages of research | Later stages of research |
| Typical methods | Experiments, quasi-experiments | Interviews, observations | Surveys, case studies |
Research designs at a glance
Causal research explains why something happens, whereas descriptive research explains what is happening.
Meaning of Causal Research and Its Components
Causal studies focus on an analysis of a situation or a specific problem to explain the patterns of relationships between variables. Causal research can be conducted in order to assess impacts of specific changes on existing norms, various processes etc. Experiments are the most popular primary data collection methods in studies with causal research design.
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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.
Causal Research Methods
The following are main methods for causal research:
1. Experiments. Experiments refer to manipulating an independent variable and observing its effects on a dependent variable while controlling for other factors. Experiments are considered the gold standard of causal research due to their ability to provide strong evidence for cause-and-effect relationships. Unlike other methods that observe correlations, experiments actively manipulate independent variables to observe their impact on dependent variables, allowing researchers to isolate and measure the causal effect.
2. Quasi-Experiments. These is utilizing existing natural variations in independent variables and comparing outcomes between groups. While experiments offer the gold standard for causal research, they are not always feasible or ethical in certain situations. This is where quasi-experiments come in. Though not as rigorous as experiments, they provide valuable insights into causal relationships when randomization is not possible.
3. Propensity Score Matching (PSM). It is a causal research technique used to evaluate the effectiveness of interventions, policies, or marketing campaigns. It aims to minimize the impact of selection bias, which occurs when the groups being compared differ in ways that can influence the outcome variable.
4. Instrumental Variables. Using an instrumental variable that is correlated with the independent variable but not directly with the outcome to estimate the causal effect. Instrumental variables are a powerful tool in causal research, allowing researchers to estimate the effect of an independent variable on an outcome variable even in the presence of confounding factors.
5. Regression Discontinuity Design. This method refers to Exploiting discontinuities in a variable used to assign individuals to groups to identify causal effects. Regression discontinuity design is another effective causal research method gaining traction in business studies. This design leverages naturally occurring discontinuities or cutoffs in a variable to estimate the causal effect of an intervention or treatment.
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
- Evaluating the impact of digital marketing campaigns on customer engagement
- Investigating whether flexible working patterns improve organisational performance
Advantages and Disadvantages
One of the major advantages of causal research is its ability to provide evidence-based explanations for business decisions. Causal research helps organisations understand not only what is happening, but also why it is happening.
Another advantage is the relatively high level of internal validity associated with controlled research designs and systematic analysis. Causal studies can also often be replicated in order to verify findings under similar conditions.
Despite its strengths, causal research has important limitations. One major challenge is that correlation does not always mean causation. Two variables may appear related simply because of coincidence or influence from other factors. 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.
Business environments are also highly complex, making it difficult to isolate variables completely. For instance, employee motivation may be influenced simultaneously by leadership style, salary, organisational culture, remote working conditions, and economic uncertainty.
As a result, causal relationships in business studies can rarely be proven with absolute certainty. In some cases, identifying which variable is the cause and which variable is the effect may also be difficult.
Causal Research in the Age of AI and Digital Business
Advances in AI, big data, and digital analytics have significantly increased the importance of causal research in business studies. Modern organisations increasingly use causal analysis to evaluate the effectiveness of AI systems, digital marketing campaigns, customer recommendation algorithms, employee performance systems, automation initiatives, and data-driven business strategies.
For example, companies may use causal research to determine whether AI-driven recommendation systems genuinely increase customer purchases or whether observed changes are influenced by other factors such as seasonal demand, pricing strategies, or economic conditions.
Similarly, organisations implementing remote working technologies may attempt to identify whether productivity improvements are actually caused by digital tools or whether other variables such as management style, employee motivation, or organisational culture are responsible for the observed outcomes.
Causal research is also becoming increasingly important in digital marketing and social media analytics. Businesses often need to determine whether increases in customer engagement, website traffic, or online sales are directly caused by specific advertising campaigns, influencer partnerships, or AI-powered targeting systems.
In modern business analytics, distinguishing causation from simple correlation remains one of the greatest challenges. AI systems can identify patterns and relationships within massive datasets very efficiently; however, detecting a statistical relationship between variables does not automatically mean that one variable causes the other.
For instance, an AI system may identify a strong relationship between employee use of collaboration software and productivity levels. Nevertheless, productivity improvements may actually be influenced by other factors such as leadership quaeality, training, or organisational restructuring. As a result, causal research plays a critical role in helping organisations avoid misleading conclusions and make more accurate evidence-based decisions in increasingly complex digital business environments.
When to Use Causal Research
You can use causal research when you reserch objective is to test cause-and-effect relationships between variables and provide evidence for management decision-making. In business studies, causal research is commonly used in areas such as marketing effectiveness, organizational performance, policy evaluation, and impact assessment of innovations such as artificial intelligence or digital transformation.
More specifically, you should use causal research if:
- you need evidence about impacts or effects
- your study aims to test hypotheses
- variables can be measured systematically
- understanding causality is important for decision-making
- the research problem requires explanation rather than simple description
Use causal research when you want to understand whether one factor actually causes changes in another factor.
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[1] Source: Zikmund, W.G., Babin, J., Carr, J. & Griffin, M. (2012) “Business Research Methods: with Qualtrics Printed Access Card” Cengage Learning