Rethinking the Win Ratio: A Causal Framework for Hierarchical Outcome Analysis
Conventional methods such as the Win Ratio lack a rigorous statistical and causal foundation for quantifying treatment effects in hierarchical multivariate outcomes, potentially leading to erroneous treatment recommendations in heterogeneous populations. Method: We establish, for the first time, a formal causal framework for the Win Ratio—defining identifiable, individual-level causal estimands—and propose a novel estimation procedure based on paired nearest-neighbor matching and doubly robust estimation. Contribution/Results: We prove theoretical consistency and double robustness of the proposed estimator. Extensive validation on synthetic data and the CRASH-3 clinical trial demonstrates substantial improvements in reliability and interpretability of treatment effect estimation. Our work provides both a principled theoretical foundation and a practical methodology for causal inference with complex, hierarchically structured multiple endpoints.