Estimation of the interpretable heterogeneous treatment effect with causal subgroup discovery in survival outcomes

Estimating heterogeneous treatment effects (HTE) for survival outcomes has gained increasing attention in precision medicine, as it captures variations in treatment efficacy among patients or subgroups. However, most existing methods conduct post-hoc subgroup identifications rather than simultaneously estimating HTE and identifying causal subgroups. In this paper, we propose an interpretable HTE estimation framework that integrates meta-learners with tree-based methods to estimate the conditional average treatment effect (CATE) for survival outcomes and identify predictive subgroups simultaneously. We evaluated the performance of our method through extensive simulation studies. We also demonstrated its application in a large randomized controlled trial (RCT) for age-related macular degeneration (AMD), a progressive polygenic eye disease, to estimate the HTE of an antioxidant and mineral supplement on time-to-AMD progression and to identify genetically defined subgroups with enhanced treatment effects. Our method offers a direct interpretation of the estimated HTE and provides evidence to support precision healthcare.