Bayesian Supervised Causal Clustering

2026-03-05

AI Summary

Identifying homogeneous subgroups with similar covariates and consistent causal effects remains a significant challenge in healthcare and policy evaluation, particularly for enabling personalized decision-making. This work proposes a Bayesian supervised causal clustering method that uniquely integrates a Bayesian framework with causal clustering, leveraging individual treatment effects as a supervisory signal to simultaneously model covariate similarity and causal heterogeneity during cluster formation. By unifying Bayesian modeling, latent variable clustering, and causal inference, the approach effectively discovers clinically actionable patient subgroups. Its validity and utility are demonstrated through extensive simulations and real-world application to data from the Third International Stroke Trial, where it successfully identifies meaningful subpopulations with distinct treatment responses.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text

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.

Problem

• supervised clustering
• treatment effect
• patient subgroups
• causal inference
• personalized decision-making

Innovation

• Bayesian Supervised Causal Clustering
• treatment effect heterogeneity
• supervised clustering
• causal inference
• patient stratification