title: Comment on Pearl: Practical implications of theoretical results for causal mediation analysis.
abstract: Mediation analysis has been extensively applied in psychological and other social science research. A number of methodologists have recently developed a formal theoretical framework for mediation analysis from a modern causal inference perspective. In Imai, Keele, and Tingley (2010), we have offered such an 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 (2014) proposed an alternative approach that is based on a set of assumptions weaker than ours. In this comment, 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 (2014). The methods discussed in this comment and many more are implemented via mediation, an open-source software (Tingley, Yamamoto, Hirose, Keele, & Imai, 2013).
authors.affiliation: Department of Politics.
authors.forename: Kosuke
authors.initials: K
authors.lastname: Imai
authors.affiliation: Department of Political Science, Penn State University.
authors.forename: Luke
authors.initials: L
authors.lastname: Keele
authors.affiliation: Department of Government, Harvard University.
authors.forename: Dustin
authors.initials: D
authors.lastname: Tingley
authors.affiliation: Department of Political Science, Massachusetts Institute of Technology.
authors.forename: Teppei
authors.initials: T
authors.lastname: Yamamoto
mesh_terms: D015984:Causality; D006801:Humans; D015233:Models, Statistical; D013223:Statistics as Topic