Semiparametric Estimation for Causal Mediation Analysis with Multiple Causally Ordered Mediators

TL;DR

This paper develops new semiparametric estimators for causal pathway effects involving multiple ordered mediators, addressing identification challenges and enabling flexible, data-adaptive analysis in complex mediation settings.

Contribution

It introduces robust and efficient estimators for path-specific effects in multivariate, causally ordered mediators under Pearl's model, extending mediation analysis capabilities.

Findings

Proposed estimators are K+2-robust and locally semiparametric efficient.

Establishes rate conditions for nuisance function estimation.

Demonstrates applicability through simulations and empirical data.

Abstract

Causal mediation analysis concerns the pathways through which a treatment affects an outcome. While most of the mediation literature focuses on settings with a single mediator, a flourishing line of research has examined settings involving multiple mediators, under which path-specific effects (PSEs) are often of interest. We consider estimation of PSEs when the treatment effect operates through K(\geq1) causally ordered, possibly multivariate mediators. In this setting, the PSEs for many causal paths are not nonparametrically identified, and we focus on a set of PSEs that are identified under Pearl's nonparametric structural equation model. These PSEs are defined as contrasts between the expectations of 2^{K+1} potential outcomes and identified via what we call the generalized mediation functional (GMF). We introduce an array of regression-imputation, weighting, and "hybrid" estimators, and, in particular, two K+2-robust and locally semiparametric efficient estimators for the GMF. The latter estimators are well suited to the use of data-adaptive methods for estimating their nuisance functions. We establish the rate conditions required of the nuisance functions for semiparametric efficiency. We also discuss how our framework applies to several estimands that may be of particular interest in empirical applications. The proposed estimators are illustrated with a simulation study and an empirical example.