作者：Han, Larry; Hou, Jue; Cho, Kelly; Duan, Rui; Cai, Tianxi
作者单位：Harvard University; Harvard T.H. Chan School of Public Health; Northeastern University; University of Minnesota System; University of Minnesota Twin Cities; US Department of Veterans Affairs; Harvard University; Harvard Medical School
摘要：Federated learning of causal estimands may greatly improve estimation efficiency by leveraging data from multiple study sites, but robustness to heterogeneity and model misspecifications is vital for ensuring validity. We develop a Federated Adaptive Causal Estimation (FACE) framework to incorporate heterogeneous data from multiple sites to provide treatment effect estimation and inference for a flexibly specified target population of interest. FACE accounts for site-level heterogeneity in the...
作者：Shao, Meijia; Xia, Dong; Zhang, Yuan
作者单位：Hong Kong University of Science & Technology; University System of Ohio; Ohio State University
摘要：U-statistics play central roles in many statistical learning tools but face the haunting issue of scalability. Despite extensive research on accelerating computation by U-statistic reduction, existing results almost exclusively focused on power analysis. Little work addresses risk control accuracy, which requires distinct and much more challenging techniques. In this article, we establish the first statistical inference procedure with provably higher-order accurate risk control for incomplete ...
