Double cross-fit doubly robust estimators: Beyond series regression

• 成果类型: Article
• 署名作者: McClean, Alec; Balakrishnan, Sivaraman; Kennedy, Edward H.; Wasserman, Larry
• 署名单位: Carnegie Mellon University
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412; 1467-9868
• DOI: 10.1093/jrsssb/qkag057
• 发表日期: 2026-09
• 页码: 1469-1491
• 关键词: double cross-fitting; Functional Estimation; Minimax Estimation; parameters; VALUES; sample

摘要

Double cross-fit doubly robust (DCDR) estimators, which train nuisance function estimators on separate samples, are effective new estimators for causal functionals. We establish several novel theoretical results for them, building on recent work. We provide a structure-agnostic error analysis, which holds with generic nuisance functions and estimators. Then, we propose n-consistent DCDR estimators with undersmoothed local polynomial regression and k-Nearest Neighbours and a minimax rate-optimal DCDR estimator with undersmoothed kernel regression. Finally, we demonstrate inference is possible even in the non-root-n regime with a central limit theorem for an undersmoothed DCDR estimator. We reinforce our theoretical results with simulation experiments.