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.
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.
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)
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)
Yamamoto, Teppei. ``Identification and Estimation of Causal Mediation Effects with Treatment Noncompliance.''
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.
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)
© Kosuke Imai