Bayesian Causal Inference for Multiple Mediators

• Activity Number: 455
• Type: Invited
• Date/Time: Wednesday, August 7, 2013 : 8:30 AM to 10:20 AM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Chanmin Kim and Michael Daniels*+ and Joe Hogan
• Companies: University of Florida and The University of Texas at Austin and Brown University
• Keywords: Mediation ; Bayesian inference ; causal inference

Abstract

In behavioral studies, the causal effect of a intervention is of interest to researchers. There have been many approaches proposed for causal mediation analysis, but mostly for the single mediator case. This is due in part to causal interpretations of multiple mediators being quite complex both in terms of identifying and interpreting appropriate causal effects. Most of these approaches rely on a sequential ignorability and no-interaction assumptions, which can be hard to justify in behavioral trials. Here, we propose a Bayesian approach to infer natural direct and indirect effects of multiple mediators. Our approach avoids the sequential ignorability assumption and allows for estimation of the indirect effects of individual mediators and the joint effects of multiple mediators.