Path-Free Decomposition for Direct, Indirect and Interaction Effects in Mediation Analysis

TL;DR

This paper introduces a novel path-free approach to decompose the total effect in mediation analysis into direct, indirect, and interaction effects, avoiding path-dependence issues present in previous methods.

Contribution

It proposes a new three-effect decomposition that separately identifies interaction effects, using conditional means and OLS estimators, unlike traditional density-based methods.

Findings

The method accurately estimates effects in simulations.

Empirical analysis demonstrates practical applicability.

Separates interaction effects from direct and indirect effects.

Abstract

Given a binary treatment and a binary mediator, mediation analysis decomposes the total effect of the treatment on an outcome variable into direct and indirect effects. However, the existing decompositions are "path-dependent", and consequently, there appeared different versions of direct and indirect effects. Differently from these, this paper proposes a "path-free" decomposition of the total effect into three sub-effects: direct, indirect, and treatment-mediator interaction effects. Whereas the interaction effect has been part of the indirect effect in the existing two-effect decompositions, it is separately identified in our three-effect decomposition. All effects are found using conditional means, but not conditional densities, and are estimated with ordinary least squares estimators. Simulation and empirical analyses are provided as well.