Methods for inferring Causality

#artificialintelligence 

In our previous article Part 1: Getting started with Causal Inference, we covered the basics of causal inference and gave a lot of attention to Regression. We also discussed that regression is the not only way to close backdoors in causal estimation design. In this article, we are going to discuss some other methods, all aiming to achieve the same thing, that is, to make treatment and control groups similar in everything except in treatment. The goal of matching is to reduce the bias for the estimated treatment effect in an observational-data study, by finding, for every treated unit, one (or more) non-treated unit(s) with similar observable characteristics against which the covariates are balanced out. If there is some confounder, say age, which affects both the treatment and outcome, thereby making treatment and control group incomparable, we can make them comparable by matching each treated unit with a similar unit from the control group.

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