5 papers
Optimally taming biases in black-box models for efficient semiparametric estimation
Yihong Gu, Qishuo Yin, Tianxi Cai +1
Modern semiparametric estimation often relies on flexible black-box machine learning methods to estimate nuisance functions, raising a fundamental question: how do nuisance estimat…
Cost-optimal Sequential Testing via Doubly Robust Q-learning
Doudou Zhou, Yiran Zhang, Dian Jin +3
Clinical decision-making often involves selecting tests that are costly, invasive, or time-consuming, motivating individualized, sequential strategies for what to measure and when…
Sampling-based federated inference for M-estimators with non-smooth objective functions
Xiudi Li, Lu Tian, Tianxi Cai
We propose a novel sampling-based federated learning framework for statistical inference on M-estimators with non-smooth objective functions, which frequently arise in modern stati…
Model-free Approach to Evaluate a Censored Intermediate Outcome as a Surrogate for Overall Survival
Xuan Wang, Tianxi Cai, Lu Tian +1
Clinical trials or studies oftentimes require long-term and/or costly follow-up of participants to evaluate a novel treatment/drug/vaccine. There has been increasing interest in th…
Nonparametric estimation of the total treatment effect with multiple outcomes in the presence of terminal events
Jessica Gronsbell, Zachary R. McCaw, Isabelle-Emmanuella Nogues +4
As standards of care advance, patients are living longer and once-fatal diseases are becoming manageable. Clinical trials increasingly focus on reducing disease burden, which can b…