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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