Finding patient subgroups with similar characteristics is crucial for personalized decision-making in various disciplines such as healthcare and policy evaluation. While most existing approaches rely on unsupervised clustering methods, there is a growing trend toward using supervised clustering methods that identify operationalizable subgroups in the context of a specific outcome of interest. We propose Bayesian Supervised Causal Clustering (BSCC), with treatment effect as outcome to guide the clustering process. BSCC identifies homogenous subgroups of individuals who are similar in their covariate profiles as well as their treatment effects. We evaluate BSCC on simulated datasets as well as real-world dataset from the third International Stroke Trial to assess the practical usefulness of the framework.

翻译：在医疗健康和政策评估等多个领域中，发现具有相似特征的患者亚群对于个性化决策至关重要。尽管现有方法大多依赖于无监督聚类方法，但一种趋势日益增长，即采用监督聚类方法来识别在特定感兴趣结果背景下的可操作亚群。我们提出了贝叶斯监督因果聚类（BSCC），以处理效应作为结果来指导聚类过程。BSCC能够识别在协变量特征和处理效应方面均具有同质性的个体亚群。我们在模拟数据集以及来自第三次国际卒中试验的真实世界数据集上评估了BSCC，以评估该框架的实际效用。