A Bayesian Nonparametric Approach to Mediation and Spillover Effects with Multiple Mediators in Cluster-Randomized Trials
针对聚类随机试验中多个非结构化中介变量带来的因果推断挑战，提出新的溢出中介效应估计量，并开发嵌套依赖狄利克雷过程混合先验来灵活建模，通过模拟和实例验证方法有效性。
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 to flexibly capture the outcome and mediator surfaces at different levels. We conduct extensive simulations across various scenarios to evaluate the frequentist performance of our methods, compare them with a Bayesian parametric counterpart and illustrate our new methods in an analysis of a completed CRT. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.