A0489
Title: A Bayesian nonparametric approach to mediation and spillover effect with multiple mediators in cluster-randomized trials
Authors: Fan Li - Yale University (United States) [presenting]
Yuki Ohnishi - Yale University (United States)
Abstract: 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, new causal estimands are proposed 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. Identification results are established for each estimand and, to flexibly model the complex data structures inherent in CRTs, a new Bayesian nonparametric prior is developed - the nested dependent Dirichlet process mixture - designed to flexibly capture the outcome and mediator surfaces at different levels. Extensive simulations are conducted across various scenarios to evaluate the frequentist performance of the methods, compare them with a Bayesian parametric counterpart, and illustrate the new methods in an analysis of a completed CRT.
