Causal Clustering for Treatment-Heterogeneity-Driven Disease Subtyping: with an Application to a Cardiac Magnetic Resonance Imaging Study

Music City Center 

We propose a novel statistical framework for causal clustering to identify disease phenotypes driven by heterogeneous treatment effects (HTEs), addressing the critical need for therapy optimization in complex diseases. Traditional clustering methods, which rely solely on feature similarity, often fail to account for treatment response heterogeneity. Our approach integrates estimated conditional average treatment effects (CATEs) into a supervised clustering algorithm, using a penalized latent Gaussian mixture model to prioritize features with significant treatment effect modification, and groups patients into subtypes with maximally divergent CATE distributions. We demonstrate the method's utility in ischemic cardiomyopathy (ICM), where optimal selection for revascularization and mitral valve intervention remains challenging. Using high-dimensional radiomic features from cardiac magnetic resonance (CMR) imaging alongside clinical variables, our approach successfully identifies distinct disease subtypes with differential treatment benefits. The results show enhanced patient stratification compared to traditional clustering methods, with clear implications for treatment optimization. This framework provides a statistically rigorous approach for incorporating causal effects into disease sub-phenotyping, with broad potential applications in precision medicine beyond cardiovascular disease.

Causal Machine Learning

Clustering

Medical Imaging

Radiomics

Treatment Optimization