Hypergraph-Based Doubly Robust Estimation for Causal Inference of Drug Combination Effects in Heart Failure Treatment

AO_SCPLOWBSTRACTC_SCPLOWDisease management for heart failure with preserved ejection fraction (HFpEF) requires understanding the comparative effectiveness of real-world drug combinations rather than single agents. Standard randomized controlled trials (RCTs) for multi-drug regimens are prohibitively expensive, slow, and often infeasible at scale, motivating the use of causal machine learning methods on large-scale electronic health records (EHRs). However, reliable estimation of treatment effects is challenging due to the high-order drug-drug interactions, strong confounding factors, and patient heterogeneity across sex, age, and other statuses. Existing causal machine learning methods mostly focus on comparing treatments with pairwise drug combinations. Techniques for multi-drug regimens are less studied. To fill this gap, we propose a Hypergraph-based Doubly Robust framework for HF-pEF (HyperDR), which represents six HF drug classes as nodes and observed multi-drug regimens as hyperedges, and uses a hypergraph neural network to learn shared representations for both drugs and combinations from cross-sectional EHR data. On top of these representations, we jointly train a propensity-score model and an outcome model with a doubly robust objective that combines cross-entropy losses with an augmented inverse-probability-weighted regularizer. This enables consistent treatment-effect estimation when either component is correctly specified while stabilizing learning under rare regimens. Experiments on a real-world HFpEF cohort show that HyperDR improves outcome prediction (hospitalization risk) compared with baseline methods. We also did case studies to interpret model results in treatment rankings across different patient subgroups.