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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. |
Papers that provide an accessible overview: |
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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Keele, Luke, Dustin Tingley, and
Teppei Yamamoto. ``Identifying
Mechanisms behind Policy Interventions via Causal Mediation
Analysis.'' Journal of Policy Analysis and
Management, Forthcoming. |
Papers that contain theoretical results: |
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. (2013). ``Experimental Designs for
Identifying Causal Mechanisms.'' (with
discussions) Journal of the Royal Statistical Society,
Series A (Statistics in Society), Vol. 173, No. 1
(January), pp. 5-51. (lead article) Read before the Royal
Statistical Society. |
Imai, Kosuke and Teppei
Yamamoto. (2013). ``Identification and
Sensitivity Analysis for Multiple Causal Mechanisms: Revisiting
Evidence from Framing Experiments.'' Political
Analysis, Vol. 21, No. 2 (Spring), pp. 141-171. (lead article)
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Chan, K.C.G, K. Imai, S.C.P. Yam,
Z. Zhang. ``Efficient Nonparametric
Estimation of Causal Mediation Effects.''
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Yamamoto, Teppei. ``Identification
and Estimation of Causal Mediation Effects with Treatment
Noncompliance.'' |
Imai, Kosuke, and Zhichao Jiang. ``Identification and Sensitivity
Analysis of Contagion Effects with Randomized Placebo-Controlled
Trials.'' Journal of the Royal Statistical
Society, Series A (Statistics in Society), Forthcoming. |
Papers that describe the companion software: |
Tingley, Dustin, Teppei Yamamoto, Kentaro
Hirose, Luke Keele, and Kosuke Imai. (2014). ``mediation: R Package
for Causal Mediation Analysis.'' Journal of
Statistical Software, , Vol. 59, No. 5 (August),
pp. 1-38. |
Tingley, Dustin, and Raymond
Hicks. (2011). ``Causal
Mediation Analysis.'' Stata Journal,
Vol. 11, No. 4, pp. 609-615. |
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.
|
Commentaries: |
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.
|
Imai, Kosuke. (2012). ``Comments: Improving
Weighting Methods for Causal Mediation Analysis.''
Journal of Research on Educational Effectiveness,
Vol. 5, No. 3, pp. 293-295. |
Imai, Kosuke, Dustin Tingley, and Teppei
Yamamoto. (2013). ``Reply to Discussions
of ``Experimental Designs for Identifying Causal
Mechanisms.'' Journal of the Royal Statistical
Society, Series A (Statistics in Society), Vol. 173, No. 1
(January), pp. 46-49. |
Imai, Kosuke, Luke Keele, Dustin
Tingley, and Teppei Yamamoto. (2014). ``Comment on Pearl:
Practical Implications of Theoretical Results for Causal Mediation
Analysis.'' Psychological Methods, Vol. 19,
No. 4 (December), 482-487. |
Presentation slides that are used for a
three hour workshop on causal mediation at the 2013 Annual
Meeting of American Political Science Association are available
for download from here. |
Tingley, Dustin, Teppei Yamamoto, Kentaro
Hirose, Luke Keele, and Kosuke Imai. ``mediation: R Package for
Causal Mediation Analysis.'' available through The
Comprehensive R Archive Network. 2009-2014.
To install type ** install.packages("mediation") ** in the latest
version of R. A useful review article by Adam Sales has appeared
in Journal of Educational and Behavioral Statistics is available
here.
|
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) |