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Causal mediation analysis is routinely conducted by applied researchers in avariety of disciplines. The goal of such an analysis is to investigatealternative causal mechanisms by examining the roles of intermediate variablesthat lie in the causal paths between the treatment and outcome variables. Inthis paper we first prove that under a particular version of sequentialignorability assumption, the average causal mediation effect (ACME) isnonparametrically identified. We compare our identification assumption withthose proposed in the literature. Some practical implications of ouridentification result are also discussed. In particular, the popular estimatorbased on the linear structural equation model (LSEM) can be interpreted as anACME estimator once additional parametric assumptions are made. We show thatthese assumptions can easily be relaxed within and outside of the LSEMframework and propose simple nonparametric estimation strategies. Second, andperhaps most importantly, we propose a new sensitivity analysis that can beeasily implemented by applied researchers within the LSEM framework. Like theexisting identifying assumptions, the proposed sequential ignorabilityassumption may be too strong in many applied settings. Thus, sensitivityanalysis is essential in order to examine the robustness of empirical findingsto the possible existence of an unmeasured confounder. Finally, we apply theproposed methods to a randomized experiment from political psychology. We alsomake easy-to-use software available to implement the proposed methods.

Identification, Inference and Sensitivity Analysis for Causal Mediation Effects