Can Correlation Be Used to Infer Causality?

Correlation and causality are often confused in data analysis and decision-making processes. This situation, which seems like a simple confusion, can lead to wrong decisions and damage companies when not paid attention. So what is correlation, what is the difference between causality and correlation, how is causality determined? Let's examine it together.

What is Correlation?

Correlation is a statistical term that allows us to interpret the strength and direction of the relationship between variables. Correlation can take a value between -1 and 1 depending on the relationship between variables. If the variables move together, the correlation has a positive value. If one variable increases while the other decreases, the correlation value will be negative. For detailed information about correlation, you can review my article titled What is Correlation?

What is Causality?

Causality is the effect of one event on the occurrence of another event. Therefore, by looking at the occurrence of one of the two events, we can make inferences about the other event. In correlation, we make inferences by examining whether two variables move together. How similar are they? But is correlation and causation the same thing? Can we say that variables with high correlation affect each other? Let's examine it together.

Why Correlation Does Not Prove Causality?

Correlation shows whether two situations or variables move together. High correlation does not mean that the variables affect each other. Two variables with high correlation may be completely independent of each other. These variables may be moving together due to another variable. For example, the correlation value between ice cream sales and air conditioner usage time data is high. There is no direct relationship between these two data. The third data that affects both of these variables is temperature. Both variables act in the same way due to the temperature variable. For this reason, causality should not be interpreted with correlation analysis.

How to Determine Causality?

One of the most effective methods used to determine causality is the A/B test method. Two groups are formed to conduct this test. One of these groups is called the experimental group and the other is called the control (Placebo) group. In the experimental group, the value of the variable whose effect is examined is changed, while no change is made in the control group. If the difference between the results in the experimental group and the control group is statistically significant, it means that there is a causal relationship between the two variables. If the difference is not significant enough, it is concluded that the change made did not affect the result and the two variables do not affect each other. For detailed information about A/B testing, you can review my article titled What is A/B Testing?
 

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