2 papers
stat.ME2026
Quantifying Individual Risk for Binary Outcomes
Peng Wu, Peng Ding, Zhi Geng +1
Understanding treatment effect heterogeneity is crucial for reliable decision-making in treatment evaluation and selection. The conditional average treatment effect (CATE) is widel…
stat.ME2026
Evaluating Surrogates in Individualized Treatment Rules
Zeyu Xu, Xiaojie Mao, Hao Mei +1
In many decision-making problems, the primary outcome is expensive, time-consuming, or difficult to observe, so individualized treatment rules (ITRs) may be instead learned from su…