3 papers
stat.ME2025
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…
stat.ME2024
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.ML2023
Constructing Synthetic Treatment Groups without the Mean Exchangeability Assumption
Yuhang Zhang, Yue Liu, Zhihua Zhang
The purpose of this work is to transport the information from multiple randomized controlled trials to the target population where we only have the control group data. Previous wor…