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An important goal of social science research is the analysis of causal mechanisms. A common framework for the statistical analysis of mechanisms has been mediation analysis, 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 collection of papers we advance the statistical analysis and experimental design of causal mechanisms in several important ways. 1) We formalize mediation analysis in terms of the well established potential outcome framework for causal inference. 2) We introduce a minimal set of assumptions thatidentify the causal mediation effects. 3) We show how to conduct sensitivity analyses to violations of this identifying assumption. Our sensitivity analysis allows researchers to ask, how large a violation would be necessary before their results would be reversed. 4) We extend our proposed methods to various types of data and statistical models. Our method can accommodate linear and nonlinear relationships, parametric and nonparametric models, continuous and discrete mediators, and different types of outcome variables. 5) We show how to design randomized experiments in order to identify causal mechanisms. 6) We provide an easy to use package in the free software language R that implements everything discussed in the papers. |
Imai, Kosuke, Luke Keele, Dustin
Tingley, and Teppei
Yamamoto. (2011). ``Unpacking
the Black Box of Causality: Learning about Causal Mechanisms
from Experimental and Observational
Studies.'' American Political Science
Review, Vol. 105, No. 4 (November), pp. 765-789.
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Imai, Kosuke, Luke Keele, and Teppei
Yamamoto. (2010). ``Identification,
Inference, and Sensitivity Analysis for Causal Mediation
Effects.'' Statistical Science, Vol. 25, No. 1
(February), pp. 51-71.
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Imai, Kosuke, Luke Keele, and Dustin
Tingley. (2010). ``A General Approach
to Causal Mediation Analysis.'' Psychological
Methods, Vol. 15, No. 4 (December), pp. 309-334. (lead
article)
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Imai, Kosuke, Dustin Tingley, and Teppei
Yamamoto. ``Experimental Designs for
Identifying Causal Mechanisms.'' (with
discussions) Journal of the Royal Statistical Society,
Series A (Statistics in Society), Forthcoming. To be read
before the Royal Statistical Society in 2012.
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Imai, Kosuke and Teppei
Yamamoto. ``Identification and
Sensitivity Analysis for Multiple Causal Mechanisms: Revisiting
Evidence from Framing Experiment.''
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Imai, Kosuke, Luke Keele, Dustin
Tingley, and Teppei Yamamoto. (2010). ``Causal Mediation
Analysis Using R,'' in Advances in Social
Science Research Using R, ed. H. D. Vinod, New York:
Springer (Lecture Notes in Statistics), pp. 129-154.
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Imai, Kosuke, Booil Jo, and Elizabeth
A. Stuart. (2011). ``Using Potential
Outcomes to Understand Causal Mediation Analysis: Comment on
Maxwell, Cole, and Mitchell (2011).''
Multivariate Behavioral Research, Vol. 46, No. 5,
pp. 842-854.
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Presentation slides that are used to
describe this project at the 2010 Summer Seminar at the
Institute of Statistical Mathematics are available for download
from here. |
Tingley, Dustin, Teppei Yamamoto, Luke
Keele, and Kosuke Imai. ``mediation: R Package
for Causal Mediation Analysis.'' available through
The
Comprehensive R Archive Network. 2009-2011.
To install type ** install.packages("mediation") ** in
the latest version of R. |
Hicks, Raymond and Dustin Tingley, mediation:
A Stata package for causal mediation analysis,''
available through the Boston
College Statistical Software Components Archive. To
install type ** ssc install mediation ** in Stata. |
National Science Foundation, (2009-2012).
``Statistical Analysis of Causal Mechanisms: Identification,
Inference, and Sensitivity Analysis,'' (Methodology, Measurement,
and Statistics Program and Political Science Program; SES-0918968) |