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Clarifying causal mediation analysis for the applied researcher: Defining effects based on what we want to learn
The incorporation of causal inference in mediation analysis has led to
theoretical and methodological advancements -- effect definitions with causal
interpretation, clarification of assumptions required for effect
identification, and an expanding array of options for effect estimation.
However, the literature on these results is fast-growing and complex, which may
be confusing to researchers unfamiliar with causal inference or unfamiliar with
mediation. The goal of this paper is to help ease the understanding and
adoption of causal mediation analysis. It starts by highlighting a key
difference between the causal inference and traditional approaches to mediation
analysis and making a case for the need for explicit causal thinking and the
causal inference approach in mediation analysis. It then explains in
as-plain-as-possible language existing effect types, paying special attention
to motivating these effects with different types of research questions, and
using concrete examples for illustration. This presentation differentiates two
perspectives (or purposes of analysis): the explanatory perspective (aiming to
explain the total effect) and the interventional perspective (asking questions
about hypothetical interventions on the exposure and mediator, or
hypothetically modified exposures). For the latter perspective, the paper
proposes tapping into a general class of interventional effects that contains
as special cases most of the usual effect types -- interventional direct and
indirect effects, controlled direct effects and also a generalized
interventional direct effect type, as well as the total effect and overall
effect. This general class allows flexible effect definitions which better
match many research questions than the standard interventional direct and
indirect effects
Identification, Inference and Sensitivity Analysis for Causal Mediation Effects
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.Comment: Published in at http://dx.doi.org/10.1214/10-STS321 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Graphical models for mediation analysis
Mediation analysis seeks to infer how much of the effect of an exposure on an
outcome can be attributed to specific pathways via intermediate variables or
mediators. This requires identification of so-called path-specific effects.
These express how a change in exposure affects those intermediate variables
(along certain pathways), and how the resulting changes in those variables in
turn affect the outcome (along subsequent pathways). However, unlike
identification of total effects, adjustment for confounding is insufficient for
identification of path-specific effects because their magnitude is also
determined by the extent to which individuals who experience large exposure
effects on the mediator, tend to experience relatively small or large mediator
effects on the outcome. This chapter therefore provides an accessible review of
identification strategies under general nonparametric structural equation
models (with possibly unmeasured variables), which rule out certain such
dependencies. In particular, it is shown which path-specific effects can be
identified under such models, and how this can be done
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