Bayesian Supervised Causal Clustering
This work is significant for healthcare and policy evaluation, aiming to improve personalized decision-making by identifying patient subgroups that respond similarly to treatments.
This paper addresses the problem of identifying patient subgroups with similar treatment effects. It proposes Bayesian Supervised Causal Clustering (BSCC) to group individuals based on both covariate profiles and treatment effects, and evaluates it on simulated and real-world datasets.
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.