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Identification and Sensitivity Analysis for Multiple Causal Mechanisms: Revisiting Evidence from Framing Experiments

Published online by Cambridge University Press:  04 January 2017

Kosuke Imai and
Teppei Yamamoto
Kosuke Imai*
Affiliation:
Department of Politics, Princeton University, Princeton NJ 08544
Teppei Yamamoto
Affiliation:
Department of Political Science, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139 e-mail: teppei@mit.edu
*
e-mail: kimai@princeton.edu (corresponding author)
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Abstract

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Social scientists are often interested in testing multiple causal mechanisms through which a treatment affects outcomes. A predominant approach has been to use linear structural equation models and examine the statistical significance of the corresponding path coefficients. However, this approach implicitly assumes that the multiple mechanisms are causally independent of one another. In this article, we consider a set of alternative assumptions that are sufficient to identify the average causal mediation effects when multiple, causally related mediators exist. We develop a new sensitivity analysis for examining the robustness of empirical findings to the potential violation of a key identification assumption. We apply the proposed methods to three political psychology experiments, which examine alternative causal pathways between media framing and public opinion. Our analysis reveals that the validity of original conclusions is highly reliant on the assumed independence of alternative causal mechanisms, highlighting the importance of proposed sensitivity analysis. All of the proposed methods can be implemented via an open source R package, mediation.

Information

Type
Research Article
Information
Political Analysis , Volume 21 , Issue 2 , Spring 2013, pp. 141 - 171
Copyright
Copyright © The Author 2013. Published by Oxford University Press on behalf of the Society for Political Methodology 

Footnotes

Authors' note: The proposed methods can be implemented via open-source software mediation that is freely available as an R package at the Comprehensive R Archive Network (CRAN, http://cran.r-project.org/package=mediation). The replication archive for this article is available online as Imai and Yamamoto (2012). We are grateful to Ted Brader, Jamie Druckman, and Rune Slothuus for sharing their data with us. We thank Dustin Tingley and Mike Tomz for useful discussions that motivated this article. John Bullock, Adam Glynn, and Tyler VanderWeele provided helpful suggestions. We also thank the Associate Editor and two anonymous referees for their comments that significantly improved the paper. An earlier version of this article was circulated under the title “Sensitivity Analysis for Causal Mediation Effects under Alternative Exogeneity Conditions.”

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