Traditional statistical inference in cluster randomized trials typically invokes the asymptotic theory that requires the number of clusters to approach infinity. In this article, we propose an alternative conformal causal inference framework for analyzing cluster randomized trials that achieves the target inferential goal in finite samples without the need for asymptotic approximations. Different from traditional inference focusing on estimating the average treatment effect, our conformal causal inference aims to provide prediction intervals for the difference of counterfactual outcomes, thereby providing a new decision-making tool for clusters and individuals in the same target population. We prove that this framework is compatible with arbitrary working outcome models -- including data-adaptive machine learning methods that maximally leverage information from baseline covariates, and enjoys robustness against misspecification of working outcome models. Under our conformal causal inference framework, we develop efficient computation algorithms to construct prediction intervals for treatment effects at both the cluster and individual levels, and further extend to address inferential targets defined based on pre-specified covariate subgroups. Finally, we demonstrate the properties of our methods via simulations and a real data application based on a completed cluster randomized trial for treating chronic pain.

翻译：整群随机试验中的传统统计推断通常依赖于要求群数趋于无穷的渐近理论。本文提出了一种替代性的保形因果推断框架，用于分析整群随机试验，该框架可在有限样本中实现目标推断目的，无需借助渐近近似。与专注于估计平均处理效应的传统推断不同，我们的保形因果推断旨在为反事实结果差异提供预测区间，从而为同一目标总体中的群组和个体提供新的决策工具。我们证明该框架兼容任意工作结果模型——包括最大限度利用基线协变量信息的数据自适应机器学习方法，并对工作结果模型的误设具有稳健性。在我们的保形因果推断框架下，我们开发了高效的计算算法，用于构建群水平和个体水平处理效应的预测区间，并进一步扩展至处理基于预设协变量亚组定义的推断目标。最后，我们通过模拟仿真和一项基于已完成的慢性疼痛治疗整群随机试验的真实数据应用，展示了所提方法的特性。