Identification, Inference and Sensitivity Analysis for Causal Mediation Effects

• Published in: Statistical science 2010-02, Vol.25 (1), p.51-71
• Main Authors: Imai, Kosuke, Keele, Luke, Yamamoto, Teppei
• Format: Article
• Language: English
• Subjects: Analytical estimating; Causal inference; causal mediation analysis; Confidence interval; direct and indirect effects; Epidemiology; Estimators; Freedom of speech; Generalized linear models; Inference; linear structural equation models; Logical givens; Parameter estimation; Political psychology; Sample size; Sensitivity analysis; sequential ignorability; Social sciences; Statistical inference; Statistical mechanics; Statistics; unmeasured confounders
• ISSN: 0883-4237; 2168-8745
• DOI: 10.1214/10-sts321

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.