A0468
Title: A case study of causal mediation using Bayesian nonparametrics and semiparametric corrections
Authors: Yuhua Zhang - University of Florida (United States) [presenting]
Abstract: A Bayesian nonparametric approach is proposed using a truncated enriched Dirichlet process mixture (EDPM) model to estimate natural direct (NDE) and indirect (NIE) effects in causal mediation analyses in the presence of post-treatment confounders. An efficient cluster reallocation Metropolis-Hasting algorithm is introduced to improve mixing in the blocked Gibbs sampler. A one-step posterior correction is implemented based on the efficient influence function for the setting. This post-processing step solves a critical problem in Bayesian nonparametrics: How to obtain reliable estimates and posteriors for a specific causal estimand of interest (the NDE and NIE) with excellent frequentist properties, such as correct coverage, from a model designed for complex joint distributions. Simulation studies are conducted to assess the method's performance, and it is applied to evaluate causal mediation effects in a weight management clinical trial.
