4,619 research outputs found
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
Survivor-complier effects in the presence of selection on treatment, with application to a study of prompt ICU admission
Pre-treatment selection or censoring (`selection on treatment') can occur
when two treatment levels are compared ignoring the third option of neither
treatment, in `censoring by death' settings where treatment is only defined for
those who survive long enough to receive it, or in general in studies where the
treatment is only defined for a subset of the population. Unfortunately, the
standard instrumental variable (IV) estimand is not defined in the presence of
such selection, so we consider estimating a new survivor-complier causal
effect. Although this effect is generally not identified under standard IV
assumptions, it is possible to construct sharp bounds. We derive these bounds
and give a corresponding data-driven sensitivity analysis, along with
nonparametric yet efficient estimation methods. Importantly, our approach
allows for high-dimensional confounding adjustment, and valid inference even
after employing machine learning. Incorporating covariates can tighten bounds
dramatically, especially when they are strong predictors of the selection
process. We apply the methods in a UK cohort study of critical care patients to
examine the mortality effects of prompt admission to the intensive care unit,
using ICU bed availability as an instrument
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Stronger instruments and refined covariate balance in an observational study of the effectiveness of prompt admission to intensive care units
Instrumental variable methods, subject to appropriate identification assumptions, enable consistent estimation of causal effects in the presence of unobserved confounding. Near–far matching has been proposed as one analytic method to improve inference by strengthening the effect of the instrument on the exposure and balancing observable characteristics between groups of subjects with low and high values of the instrument. However, in settings with hierarchical data (e.g. patients nested within hospitals), or where several covariate interactions must be balanced, conventional near–far matching algorithms may fail to achieve the requisite covariate balance. We develop a new matching algorithm, that combines near–far matching with refined covariate balance, to balance large numbers of nominal covariates while also strengthening the instrumental variable. This extension of near–far matching is motivated by a case-study that aims to identify the causal effect of prompt admission to an intensive care unit on 7-day and 28-day mortality
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