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

Disease 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 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 multi–drug interactions, strong confounding factors, and patient heterogeneity across sex, age, and other characteristics. Existing causal machine learning methods mostly focus on comparing treatments with pairwise drug combinations, while techniques for multi-drug regimens are less studied. To fill this gap, we propose a Hypergraph–based Doubly Robust framework (HyperDR), which represents 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. Our framework jointly trains a propensity score model and an outcome model using cross-entropy losses and an inverse-probability-weighted residual regularizer. Treatment effects are then estimated using an augmented inverse-probability-weighted (AIPW) estimator, which is consistent under standard causal and regularity assumptions if either the propensity scores or conditional outcome means are consistently estimated. On a semi-synthetic benchmark with known ground-truth treatment effects, HyperDR demonstrates improved causal effect recovery by achieving the lowest ATE and CATE RMSE among the evaluated methods. Experiments on two real-world HFpEF cohorts further demonstrate strong factual outcome prediction, while exploratory case studies characterize subgroup-specific treatment rankings.