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Questions concerning mediated causal effects are of great interest inpsychology, cognitive science, medicine, social science, public health, andmany other disciplines. For instance, about 60% of recent papers published inleading journals in social psychology contain at least one mediation test(Rucker, Preacher, Tormala, & Petty, 2011). Standard parametric approaches tomediation analysis employ regression models, and either the "difference method"(Judd & Kenny, 1981), more common in epidemiology, or the "product method"(Baron & Kenny, 1986), more common in the social sciences. In this paper wefirst discuss a known, but perhaps often unappreciated fact: that theseparametric approaches are a special case of a general counterfactual frameworkfor reasoning about causality first described by Neyman (1923), and Rubin(1974), and linked to causal graphical models by J. Robins (1986), and Pearl(2000). We then show a number of advantages of this framework. First, it makesthe strong assumptions underlying mediation analysis explicit. Second, itavoids a number of problems present in the product and difference methods, suchas biased estimates of effects in certain cases. Finally, we show thegenerality of this framework by proving a novel result which allows mediationanalysis to be applied to longitudinal settings with unobserved confounders.

Counterfactual Graphical Models for Longitudinal Mediation Analysis with Unobserved Confounding