贝叶斯监督因果聚类
Bayesian Supervised Causal Clustering
AI总结：
本文提出贝叶斯监督因果聚类方法，通过治疗效应指导聚类过程，识别具有相似协变量特征和治疗效应的同质亚组，并在模拟和现实数据上验证其有效性。
摘要：
在医疗和政策评估等各个领域中，发现具有相似特征的患者亚组对于个性化决策至关重要。尽管大多数现有方法依赖于无监督聚类方法，但越来越多的研究倾向于使用监督聚类方法，以在特定感兴趣结果的背景下识别可操作的亚组。我们提出了贝叶斯监督因果聚类（BSCC），以治疗效应作为结果来指导聚类过程。BSCC识别出具有相似协变量特征以及治疗效应的同质亚组。我们在模拟数据集以及第三届国际中风试验的现实世界数据集上评估了BSCC，以评估该框架的实际实用性。
英文摘要：
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.
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