Open Access
February 2010 Identification, Inference and Sensitivity Analysis for Causal Mediation Effects
Kosuke Imai, Luke Keele, Teppei Yamamoto
Statist. Sci. 25(1): 51-71 (February 2010). 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.


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Kosuke Imai. Luke Keele. Teppei Yamamoto. "Identification, Inference and Sensitivity Analysis for Causal Mediation Effects." Statist. Sci. 25 (1) 51 - 71, February 2010.


Published: February 2010
First available in Project Euclid: 3 August 2010

zbMATH: 1328.62478
MathSciNet: MR2741814
Digital Object Identifier: 10.1214/10-STS321

Keywords: Causal inference , causal mediation analysis , direct and indirect effects , linear structural equation models , sequential ignorability , unmeasured confounders

Rights: Copyright © 2010 Institute of Mathematical Statistics

Vol.25 • No. 1 • February 2010
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