An important goal when analyzing the causal effect of a treatment on an outcome is to understand the mechanisms through which the treatment causally works. We define a causal mechanism effect of a treatment and the causal effect net of that mechanism using the potential outcomes framework. These effects provide an intuitive decomposition of the total effect that is useful for policy purposes. We offer identification conditions based on an unconfoundedness assumption to estimate them, within a heterogeneous effect environment, and for the cases of a randomly assigned treatment and when selection into the treatment is based on observables. Two empirical applications illustrate the concepts and methods.
Causal inference causal mechanisms post-treatment variables principal stratification