Easy-to-Implement Two-Way Effect Decomposition for Any Outcome Variable with Endogenous Mediator

TL;DR

This paper introduces a simple, flexible method for mediation analysis that handles endogenous mediators using instrumental variables, applicable to various outcome types, and simplifies estimation compared to traditional methods.

Contribution

It develops a nonparametric causal reduced form for outcomes with endogenous mediators, enabling straightforward effect estimation with standard regression techniques.

Findings

Method effectively estimates direct and indirect effects.

Applicable to any outcome variable type.

Simulation and empirical results validate approach.

Abstract

Given a binary treatment D and a binary mediator M, mediation analysis decomposes the total effect of D on an outcome Y into the direct and indirect effects. Typically, both D and M are assumed to be exogenous, but this paper allows M to be endogenous while maintaining the exogeneity of D, which holds certainly if D is randomized. The endogeneity problem of M is then overcome using a binary instrumental variable Z. We derive a nonparametric "causal reduced form (CRF)" for Y with either (D,Z,DZ) or (D,M,DZ) as the regressors. The CRF enables estimating the direct and indirect effects easily with ordinary least squares or instrumental variable estimator, instead of matching or inverse probability weighting that have difficulties in finding the asymptotic distribution or in dealing with near-zero denominators. Not just this ease in implementation, our approach is applicable to any Y (binary, count, continuous, etc.). Simulation and empirical studies illustrate our approach.