A Bayesian nonparametric approach to causal mediation analysis in CRTs with multiple mediators

Music City Center 

Cluster randomized trials (CRTs) with multiple unstructured mediators present significant methodological challenges for causal inference due to within-cluster correlation, interference among units, and the complexity introduced by multiple mediators. Existing causal mediation methods often fall short in simultaneously addressing these complexities, particularly in disentangling mediator-specific effects under interference that are central to studying complex mechanisms. To address this gap, we propose new causal estimands for spillover mediation effects that differentiate the roles of each individual's own mediator and the spillover effects resulting from interactions among individuals within the same cluster. We establish identification results for each estimand and, to flexibly model the complex data structures inherent in CRTs, we develop a new Bayesian nonparametric prior---the Nested Dependent Dirichlet Process Mixture---designed for flexibly capture the outcome and mediator surfaces at different levels. We illustrate our methods our new methods in an analysis of a completed CRT.

Bayesian causal inference

Bayesian Nonparametrics

Interference

Multiple mediators

Spillover Mediation Effect 

Section on Statistics in Epidemiology