Causal Mediation

Causal inference aims to identify and quantify a causal effect. With traditional causal inference methods, we can estimate the overall effect of a treatment on an outcome. When we want to better understand a causal effect, we can use causal mediation analysis to decompose the effect into a direct effect of the treatment on the outcome and an indirect effect through another variable, the mediator. Causal mediation analysis can be performed in many situations—the outcome and mediator variables may be continuous, binary, or count, and the treatment variable may be binary, multivalued, or continuous. In this talk, I will introduce the framework for causal mediation analysis and demonstrate how to perform this analysis with the mediate command, which was introduced in Stata 18. Examples will include various combinations outcome, mediator, and treatment types.