Causal mediation analysis using Stata

Causal inference studies are designed to identify and quantify the effect of a treatment (T) on an outcome of interest (Y). Sometimes, the treatment has an effect on a third variable, called a mediating variable (M), which also influences the outcome. So the treatment may have both a direct effect on the outcome (T -> Y) and an indirect effect on the outcome through its influence on the mediating variable (T -> M -> Y). The goal of causal mediation analysis is to identify and quantify these direct and indirect effects. This talk will introduce the concepts and jargon of causal mediation analysis, demonstrate how to analyze these kinds of data using Stata's mediate command, and show how to interpret and visualize these kinds of relationships.