AUGMENTED MINIMAX LINEAR ESTIMATION

• 成果类型: Article
• 署名作者: Hirshberg, David A.; Wager, Stefan
• 署名单位: Stanford University
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/21-AOS2080
• 发表日期: 2021
• 页码: 3206-3227
• 关键词: propensity score; nonparametric-estimation; Semiparametric Efficiency; statistical estimation; confidence-intervals; models; inference; regularization

摘要

Many statistical estimands can expressed as continuous linear functionals of a conditional expectation function. This includes the average treatment effect under unconfoundedness and generalizations for continuous-valued and personalized treatments. In this paper, we discuss a general approach to estimating such quantities: we begin with a simple plug-in estimator based on an estimate of the conditional expectation function, and then correct the plugin estimator by subtracting a minimax linear estimate of its error. We show that our method is semiparametrically efficient under weak conditions and observe promising performance on both real and simulated data.