What is Causal Analysis?
Causal analysis is the process of identifying and addressing the causes and effects of a phenomenon, problem, or event. It is about figuring out how one variable (the cause) affects or determines another variable (the effect), as well as recognizing the relationships between various occurrences and how changes in one variable might affect another. For example, smoking causes lung cancer, or increasing the price of a product reduces its demand. To get useful conclusions from data, this technique is frequently applied in disciplines including science, economics, and medicine. Causal analysis can help you answer questions such as:
- Why did something happen?
- What are the consequences of something happening?
- How can something be prevented or improved?
- What are the best alternatives or solutions?
To perform causal analysis, you need to collect and analyze data that can support or refute your causal hypotheses. It is important to take into account additional variables that might impact the result, including moderating, mediating, and confounding variables. These are factors that might affect or interfere with the cause-and-effect causal relationship.
Depending on your research topic, data, and context, you may apply one of several methods of causal analysis. Among the most common types are:
- Experimental research: In experimental research, one variable (the independent variable) is manipulated, and the impact of this manipulation on another variable (the dependent variable) is monitored under controlled conditions. For instance, you may run an experiment to see how patients’ blood pressure responds to various medicine dosages.
- Quasi-experimental research: Comparable to experimental study, quasi-experimental research does not randomly assign people to various groups or circumstances. Rather, it takes advantage of pre-existing groupings or natural environments that are comparable yet distinct. One way to compare is the academic achievement of pupils from different schools or with different teachers.
- Correlational research: Research that measures the direction and degree of a link between two or more variables without changing them is known as correlational research. One can quantify the relationship, for instance, between students’ study hours and grades.
- Case study research: Case study research examines one or a few situations in-depth to determine the reasons behind and consequences of each. To get insight from their experiences and approaches, you may, for instance, undertake a case study of a project that went wrong or a successful firm.
How to perform Causal Analysis?
Causal analysis is a powerful technique that can help you understand why something happens and how to prevent or improve it, in other words, it helps us understand the relationships between different events or variables. Causal analysis can offer insightful information when doing research, fixing issues, or making judgments.
In this article, we’ll break down the concept of causal analysis, step by step, catering to beginners who are new to this intriguing field.
Table of Content
- What is Causal Analysis?
- How to Perform Causal Analysis?
- Steps to Perform Casual Analysis
- What are the Benefits of Causal Analysis?
- Example Case of Causal Analysis
- Example 1: Causal Analysis with a Synthetic Dataset
- Example 2: Propensity Score Matching
- Example3: using CasualPY(Public)
- Tips for Performing Causal Analysis