Causality Analysis

Causality analysis is a concept in statistics that focuses on two key aspects-cause and effect. For instance, if you keep failing exams even after preparing so well, you may want to re-evaluate your reading strategies to understand where you could be going wrong. In that case, failure is the effect, and the mistake you are looking to identify is the cause. Similarly, if your car breaks down frequently (effect), what could be the reason (cause)?

So in causality analysis, we bring in about four ideas:

• Correlation-between the cause and effect
• Time sequence-because the cause must happen before the effect.
• Information-theoretical aspect-the assumption that the effect is as a result of a possible cause
• Need to eliminate the cause-ultimately, the goal is to do away with the cause, so we don՚t have the undesirable effect(s).

Now that you have understood the basics, let us dive deeper into some key concepts in causality analysis.

Confounding Factors/Variables

Confounding factors or variables are those variables that are related to both the exposure and the outcome. But let us look at an example to put that into perspective.

Assume we have two groups:

• The exposed group that drinks a lot of alcohol
• The unexposed group that consumes less or no alcohol

We know high alcohol consumption results in coronary heart attacks, right? However, low alcohol consumption has no negative effect.

Also, let us assume people who consume high levels of alcohol tend to use cigarettes as well. However, those that rarely drink also don՚t smoke frequently.

In such a scenario, if the individual in the first category develops coronary heart attack, it could be challenging to know if it resulted from alcohol or cigarette, right?

So the cigarette, in this case, becomes the confounding factor. Reason? It has an effect on both the exposure (alcohol) and the outcome (coronary heart attack).

Anteceding and Intervening Variables

Anteceding variables are independent variables that come before other variables in time. That is, they precede the main variables and can be considered secondary.

On the other side, an intervening variable occurs between the cause (independent variable) and effect (dependent variable).

For instance, science has it that high sugar consumption causes obesity, which often results in heart attacks. In this case, the independent variable is high sugar consumption, while the dependent variable is a heart attack. Therefore, high sugar consumption becomes out anteceding variable while obesity becomes the intervening variable.

Mediator and Moderator Elements/Variables

A mediator/mediating variable tells us how the independent and dependent variables relate, while the moderator/moderating variable affects how the two relate with each other.

Again, let us look at an example

What if the amount you spend on social media causes you to social-compare, and the rate at which you social-compare is affected by your self-esteem? In this case, self-esteem affects your level of social comparison indirectly. Therefore, it is the mediating element.

Suppose when you get tired, you become passive-aggressive, and that causes you to have arguments with those around you. In that case, being "passive-aggressive" is the mediating element because it affects the process of having argumentations.

But how do we know if the mediating element actually mediates between the independent and dependent variables?

The Sobel՚s Z-Test

Sobel՚s Z-Test proves that the independent variable is not sufficient enough to lead to the outcome-there has to be some mediation with another factor. In the above case, it՚s only passive aggression that could result in arguments, not necessarily tiredness.

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