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…
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…
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…