Causal mediation analysis is routinely conducted by applied
researchers in a variety of disciplines including epidemiology,
political science, psychology, and sociology. The goal of such an
analysis is to investigate alternative causal mechanisms by
examining the roles of intermediate variables that lie in the causal
path 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 identifying
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
if the linearity and no-interaction assumptions
are satisfied in addition to the proposed assumption.
We show that this assumption can easily be relaxed within the
framework of LSEM. Second, we consider a simple nonparametric
estimator of the ACME in order to relax distributional and
functional form assumptions. We also discuss a more general
nonparametric approach. Third, we propose a new sensitivity
analysis that can be easily implemented by applied researchers
within the standard LSEM framework. Like the existing identifying
assumptions, the proposed 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.
(Last Revised July, 2009)
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