Semiparametric Bayesian Inference for Causal Mediation in Cluster Randomized Trials

Oregon Convention Center 

We propose semiparametric Bayesian inference for causal mediation analysis in the context of cluster randomized trials (CRTs). Our approach allows for the estimation of direct and indirect effects at both the individual and cluster-levels. To model the joint distribution of cluster-level and individual-level confounders, we specify a two-stage Bayesian bootstrap (BB) with a "distance" metric between clusters, which avoids the need for restrictive parametric assumptions and allows us to borrow more information from "closer" clusters. By combining the observed data with causal assumptions, we are able to identify and estimate the natural direct and indirect effects at the individual-level and cluster-level separately. We assess the performance of our approach through simulation studies and use it to assess mediation in a cluster randomized trial in Kenya.

Biometrics Section