Counterfactual Graphical Models for Longitudinal Mediation Analysis with Unobserved Confounding

TL;DR

This paper introduces a counterfactual graphical model framework for longitudinal mediation analysis that explicitly handles unobserved confounding, improving upon traditional parametric methods in causal inference.

Contribution

It extends mediation analysis to longitudinal data with unobserved confounders using a novel counterfactual graphical approach, clarifying assumptions and reducing bias.

Findings

Framework makes assumptions explicit

Avoids bias in effect estimates

Applicable to longitudinal studies with unobserved confounders

Abstract

Questions concerning mediated causal effects are of great interest in psychology, cognitive science, medicine, social science, public health, and many other disciplines. For instance, about 60% of recent papers published in leading journals in social psychology contain at least one mediation test (Rucker, Preacher, Tormala, & Petty, 2011). Standard parametric approaches to mediation 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 we first discuss a known, but perhaps often unappreciated fact: that these parametric approaches are a special case of a general counterfactual framework for 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 makes the strong assumptions underlying mediation analysis explicit. Second, it avoids a number of problems present in the product and difference methods, such as biased estimates of effects in certain cases. Finally, we show the generality of this framework by proving a novel result which allows mediation analysis to be applied to longitudinal settings with unobserved confounders.