arXiv Machine Learning August 7, 2026 By Yuan Feng, Shiyu Shu, Yixin Fang, Ionut Bebu, Toshimitsu Hamasaki, Scott Evans, Guoqing Diao

A Unified Causal Inference Framework for the Desirability of Outcome Ranking Paradigm in Benefit-Risk Evaluation

Read the original on arXiv Machine Learning →

arXiv:2608. 05244v1 Announce Type: cross Abstract: We developed a unified covariate-adjusted causal inference framework for estimating the desirability of outcome ranking (DOOR) probability for benefit-risk evaluation in randomized trials and observational studies.

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