In cluster-randomized experiments, there is emerging interest in exploring the causal mechanism in which a cluster-level treatment affects the outcome through an intermediate outcome. Despite an extensive development of causal mediation methods in the past decade, only a few exceptions have been considered in assessing causal mediation in cluster-randomized studies, all of which depend on parametric model-based estimators. In this article, we develop the formal semiparametric efficiency theory to motivate several doubly-robust methods for addressing several mediation effect estimands corresponding to both the cluster-average and the individual-level treatment effects in cluster-randomized experiments--the natural indirect effect, natural direct effect, and spillover mediation effect. We derive the efficient influence function for each mediation effect, and carefully parameterize each efficient influence function to motivate practical strategies for operationalizing each estimator. We consider both parametric working models and data-adaptive machine learners to estimate the nuisance functions, and obtain semiparametric efficient causal mediation estimators in the latter case. Our methods are illustrated via extensive simulations and two completed cluster-randomized experiments.

翻译：在整群随机实验中，探索集群层面处理通过中间结果影响结果的因果机制日益受到关注。尽管过去十年因果中介方法已取得广泛进展，但针对整群随机研究中因果中介评估的少量例外情形仍依赖于基于参数模型的估计量。本文发展正式的半参数效率理论，以激励多种双重稳健方法，用于处理整群随机实验中对应集群平均效应和个体层面处理效应的多个中介效应估计目标——包括自然间接效应、自然直接效应和溢出中介效应。我们推导了每个中介效应的有效影响函数，并仔细参数化每个有效影响函数，以激发可操作化的实际估算策略。我们同时考虑参数工作模型和自适应数据机器学习方法来估计干扰函数，并在后一种情形下获得半参数有效的因果中介估计量。通过大量模拟实验和两项已完成的整群随机实验验证了所提方法。