no code implementations • 20 Jan 2024 • Shenbo Xu, Raluca Cobzaru, Bang Zheng, Stan N. Finkelstein, Roy E. Welsch, Kenney Ng, Ioanna Tzoulaki, Zach Shahn
In this work, we develop censoring unbiased transformations (CUTs) for survival outcomes both with and without competing risks. After converting time-to-event outcomes using these CUTs, direct application of HTE learners for continuous outcomes yields consistent estimates of heterogeneous cumulative incidence effects, total effects, and separable direct effects.
no code implementations • 3 May 2023 • Shenbo Xu, Bang Zheng, Bowen Su, Stan Finkelstein, Roy Welsch, Kenney Ng, Ioanna Tzoulaki, Zach Shahn
Estimators targeting overlap weighted effects have been proposed to address the challenge of poor overlap, and methods enabling flexible machine learning for nuisance models address model misspecification.