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.
Causal mediation analysis is routinely conducted by applied researchers in a variety of disciplines. The goal of such an analysis is to investigate alternative causal mechanisms by examining the roles of intermediate variables that lie in the causal paths between the treatment and outcome variables. In this paper, we first prove that under a particular version of sequential ignorability assumption, the average causal mediation effect (ACME) is nonparametrically identified. We compare our identification assumption with those proposed in the literature. Some practical implications of our identification result are also discussed. In particular, the popular estimator based on the linear structural equation model (LSEM) can be interpreted as an ACME estimator once additional parametric assumptions are made. We show that these assumptions can easily be relaxed within and outside of the LSEM framework and propose simple nonparametric estimation strategies. Second, and perhaps most importantly, we propose a new sensitivity analysis that can be easily implemented by applied researchers within the LSEM framework. Like the existing identifying assumptions, the proposed sequential ignorability assumption may be too strong in many applied settings. Thus, sensitivity analysis is essential in order to examine the robustness of empirical findings to the possible existence of an unmeasured confounder. Finally, we apply the proposed methods to a randomized experiment from political psychology. We also make easy-to-use software available to implement the proposed methods. |
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 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.
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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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Chan, K.C.G, K. Imai, S.C.P. Yam, Z. Zhang
``Efficient Nonparametric Estimation of Causal Mediation Effects.''
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