Identification, Inference and Sensitivity Analysis for Causal Mediation Effects

TL;DR

This paper establishes nonparametric identification of causal mediation effects under certain assumptions, compares these with existing assumptions, and introduces a new sensitivity analysis method with practical applications and software tools.

Contribution

It proves nonparametric identification of ACME under sequential ignorability, relaxes parametric assumptions, and develops an accessible sensitivity analysis approach.

Findings

ACME is nonparametrically identified under specific assumptions

Proposed sensitivity analysis assesses robustness to unmeasured confounders

Applied methods to a political psychology experiment with software implementation

Abstract

Causal mediation analysis is routinely conducted by applied researchers in a variety of disciplines. The goal of such an analysis is to investigate alternative causal mechanisms by examining the roles of intermediate variables that lie in the causal paths between the treatment and outcome variables. In this paper we first prove that under a particular version of sequential ignorability assumption, the average causal mediation effect (ACME) is nonparametrically identified. We compare our identification assumption with those proposed in the literature. Some practical implications of our identification result are also discussed. In particular, the popular estimator based on the linear structural equation model (LSEM) can be interpreted as an ACME estimator once additional parametric assumptions are made. We show that these assumptions can easily be relaxed within and outside of the LSEM framework and propose simple nonparametric estimation strategies. Second, and perhaps most importantly, we propose a new sensitivity analysis that can be easily implemented by applied researchers within the LSEM framework. Like the existing identifying assumptions, the proposed sequential ignorability assumption may be too strong in many applied settings. Thus, sensitivity analysis is essential in order to examine the robustness of empirical findings to the possible existence of an unmeasured confounder. Finally, we apply the proposed methods to a randomized experiment from political psychology. We also make easy-to-use software available to implement the proposed methods.