标题： 使用贝叶斯非参数方法在具有治疗后混杂因素的集群随机试验中识别和估计因果机制 显示英文标题

标题： Identification and estimation of causal mechanisms in cluster-randomized trials with post-treatment confounding using Bayesian nonparametrics

摘要： 在集群随机试验（CRTs）中进行因果中介分析对于解释集群水平干预如何影响个体结果至关重要，但会受到干扰、治疗后混杂因素和分层协变量调整的复杂影响。我们开发了一个贝叶斯非参数框架，同时容纳干扰和一个在中介变量之前的治疗后混杂因素。通过多变量高斯耦合函数实现识别，该函数用单个依赖参数替代了跨世界独立性，从而产生对残留治疗后混杂因素的内置敏感性分析。在估计方面，我们引入了一个嵌套公共原子丰富狄利克雷过程（CA-EDP）先验，该先验整合了公共原子模型（CAM），在跨集群共享信息的同时捕捉集群间和集群内异质性，并且一个丰富狄利克雷过程（EDP）结构实现了稳健的协变量调整而不影响结果模型。我们通过推导模型的关键分布属性，包括其部分可交换划分结构，并通过建立基于实际截断的后验推断策略的收敛保证，为我们的先验提供了正式的理论支持。我们在模拟中展示了所提出方法的性能，并通过重新分析一个已完成的CRT进一步说明。 显示英文摘要

摘要： Causal mediation analysis in cluster-randomized trials (CRTs) is essential for explaining how cluster-level interventions affect individual outcomes, yet it is complicated by interference, post-treatment confounding, and hierarchical covariate adjustment. We develop a Bayesian nonparametric framework that simultaneously accommodates interference and a post-treatment confounder that precedes the mediator. Identification is achieved through a multivariate Gaussian copula that replaces cross-world independence with a single dependence parameter, yielding a built-in sensitivity analysis to residual post-treatment confounding. For estimation, we introduce a nested common atoms enriched Dirichlet process (CA-EDP) prior that integrates the Common Atoms Model (CAM) to share information across clusters while capturing between- and within-cluster heterogeneity, and an Enriched Dirichlet Process (EDP) structure delivering robust covariate adjustment without impacting the outcome model. We provide formal theoretical support for our prior by deriving the model's key distributional properties, including its partially exchangeable partition structure, and by establishing convergence guarantees for the practical truncation-based posterior inference strategy. We demonstrate the performance of the proposed methods in simulations and provide further illustration through a reanalysis of a completed CRT.