Bayesian Supervised Causal Clustering

TL;DR

This paper introduces Bayesian Supervised Causal Clustering (BSCC), a novel method for identifying patient subgroups with similar covariate profiles and treatment effects, enhancing personalized decision-making.

Contribution

The paper presents BSCC, a new supervised clustering approach that incorporates treatment effects to find meaningful subgroups, advancing personalized analysis in healthcare and policy.

Findings

BSCC effectively identifies homogeneous subgroups in simulated data.

BSCC reveals clinically relevant patient subgroups in stroke trial data.

The framework improves subgroup detection compared to unsupervised methods.

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.