Abstract

Mediation analysis has been extensively applied in psychological and other social science research. Recently, a number of methodologists have developed a formal theoretical framework for mediation analysis from a modern causal inference perspective. In Imai et al. (2010) , we have offered one such approach to causal mediation analysis that formalizes identification, estimation, and sensitivity analysis in a single framework. This approach has been used by a number of substantive researchers and in subsequent work we have also further extended it to more complex settings and developed new research designs. In an insightful article, Pearl (2013) proposes an alternative approach that are based on a set of assumptions weaker than ours. In this commentary, we demonstrate that the theoretical differences between our identification assumptions and his alternative conditions are likely to be of little practical relevance in the substantive research settings faced by most psychologists and other social scientists. We also show that our proposed estimation algorithms can be easily applied in the situations discussed in Pearl (2013). The methods discussed in this commentary and many more are implemented via open-source software mediation (Tingley et al., 2013).

Software

mediation: R Package for Causal Mediation Analysis — CRAN / GitHub

Related Papers

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.

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.

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.

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.

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.

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.

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, 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.

Chan, K.C.G, K. Imai, S.C.P. Yam, Z. Zhang ``Efficient Nonparametric Estimation of Causal Mediation Effects.''