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.
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 corresponding path coefficients. However, this approach implicitly assumes that the multiple mechanisms are causally independent of one another. In this paper, 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 . |
Imai, Kosuke, and Zhichao Jiang
(2020).
``Identification and Sensitivity Analysis of Contagion Effects in Randomized Placebo-Controlled Trials.''
Journal of the Royal Statistical Society, Series A (Statistics in Society), Vol. 183, No. 4 (October), pp. 1637-1657.
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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.
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Tingley, Dustin, Teppei Yamamoto, 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.
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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. 176, No. 1 (January), pp. 5-51. Read before the Royal Statistical Society in March, 2012.
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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. Reprinted in Advances in Political Methodology, R. Franzese, Jr. ed., Edward Elger, 2017.
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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.
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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, 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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Chan, K.C.G, K. Imai, S.C.P. Yam, Z. Zhang
``Efficient Nonparametric Estimation of Causal Mediation Effects.''
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