Introduction

Causal Analysis is the study of causation as opposed to correlation. Given two events A and B which appear to be correlated, can we determine with a certain statistical significance if one of them is the cause of another?

We can proceed with simple A/B testing, determing normality, homogenity and finally conducting a t-test to determine if there is any statistical significance in the difference between control and test group outcomes.

However, this is usually complicated by several factors, including the fact that another event or set of events might be correlated with both event A and B (confounders) or that we might need to find an Instrumental Variable that is correlated with event A but not event B, and thus measuring its indirect effect on B will indicate the causation of A on B.

I have learned to use the package DoubleML and various statistical techniques for conducting Causal Analysis.