基于因果效应的贝叶斯聚类，帮医生找相似患者亚群
Bayesian Supervised Causal Clustering
- 用治疗效果引导聚类，兼顾特征与疗效相似性
- 在真实中风试验数据上验证，识别出疗效一致的患者亚群
- 适合医疗决策、政策评估等需个性化分组的场景
在医疗和政策评估等领域，发现具有相似特征的患者亚群对个性化决策至关重要。现有方法多采用无监督聚类，而监督聚类则更关注特定结果下的可操作亚群。本文提出贝叶斯监督因果聚类（BSCC），以处理效应作为聚类目标，识别出在协变量特征和处理效应上均相似的同质群体。我们在模拟数据及第三国际中风试验（IST-3）的真实数据集上评估该框架，验证其在实际应用中的有效性。
原文摘要 · Abstract (English)
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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