[2603.05288] Bayesian Supervised Causal Clustering

Statistics > Machine Learning arXiv:2603.05288 (stat) [Submitted on 5 Mar 2026] Title:Bayesian Supervised Causal Clustering Authors:Luwei Wang, Nazir Lone, Sohan Seth View a PDF of the paper titled Bayesian Supervised Causal Clustering, by Luwei Wang and 2 other authors View PDF HTML (experimental) Abstract: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. Subjects: Machine Learning (stat.ML); Machine Learning (cs.LG) Cite as: arXiv:2603.05288 [stat.ML]   (or arXiv:2603.05288v1 [stat.ML] for this version)   https://doi.org/10.48550/arXiv.2603.05288 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Luwei Wang [view email] [v1] Thu, 5 Mar 2026 15:30:36 UTC (13,627 KB) Full-text...