Adversarial Estimation of Riesz Representers

• 成果类型: Article
• 署名作者: Chernozhukov, Victor; Newey, Whitney K.; Singh, Rahul; Syrgkanis, Vasilis
• 署名单位: Massachusetts Institute of Technology (MIT); Harvard University; Harvard University; Stanford University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459; 1537-274X
• DOI: 10.1080/01621459.2025.2588833
• 发表日期: 2026-04-03
• 页码: 1398-1409
• 关键词: Critical radius; Neural Network; Random Forest; Reproducing Kernel Hilbert Space; Semiparametric Efficiency; efficient estimation; regression-models; inference; causal; bounds

摘要

Many causal parameters are linear functionals of an underlying regression. The Riesz representer is a key component in the asymptotic variance of a semiparametrically estimated linear functional. We propose an adversarial framework to estimate the Riesz representer using general function spaces. We prove a nonasymptotic mean square rate in terms of an abstract quantity called the critical radius, then specialize it for neural networks, random forests, and reproducing kernel Hilbert spaces as leading cases. Our estimators are highly compatible with targeted and debiased machine learning with sample splitting; our guarantees directly verify general conditions for inference that allow mis-specification. We also use our guarantees to prove inference without sample splitting, based on stability or complexity. Our estimators may achieve nominal coverage in highly nonlinear simulations where some previous methods may not. They shed new light on the heterogeneous effects of matching grants. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.