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.
Traditionally in the social sciences, causal mediation analysis has been formulated, understood, and implemented within the framework of linear structural equation models. We argue and demonstrate that this is problematic for three reasons; the lack of a general definition of causal mediation effects independent of a particular statistical model, the inability to specify the key identification assumption, and the difficulty of extending the framework to nonlinear models. In this paper, we propose an alternative approach that overcomes these limitations. Our approach is general because it offers the definition, identification, estimation, and sensitivity analysis of causal mediation effects without reference to any specific statistical model. Further, our approach explicitly links these four elements closely together within a single framework. As a result, the proposed framework can accommodate linear and nonlinear relationships, parametric and nonparametric models, continuous and discrete mediators, and various types of outcome variables. The general definition and identification result also allow us to develop sensitivity analysis in the context of commonly used models, which enables applied researchers to formally assess the robustness of their empirical conclusions to violations of the key assumption. We illustrate our approach by applying it to the Job Search Intervention Study (JOBS II). We also offer easy-to-use software that implements all of our 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, 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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