English(EN) On the Asymptotic Inadmissibility of Double Machine Learning Estimators Under Structure-Agnostic Models

新研究质疑双重机器学习估计器的容许性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-07 04:00

Lin Liu及其同事的一篇新论文挑战了在结构无关（SA）模型中双重机器学习（DML）估计器的渐近不可容许性。虽然DML估计器先前已被证明在SA模型下对某些泛函是minimax的，但本研究表明，对于两个特定的泛函，DML估计器是渐近不可容许的。该论文引入了二阶估计器，特别是经验高阶影响函数（HOIF）估计器，它们在这些情况下渐近地优于DML估计器。 AI

影响这项研究有助于对机器学习中估计器性能的理论理解，可能影响更稳健的统计方法的开发。

排序理由阐述统计机器学习理论发现的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

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报道来源 [1]

arXiv stat.ML TIER_1 English(EN) · Lin Liu, Rajarshi Mukherjee, James M Robins · 2026-09-07 04:00

关于结构无关模型下双重机器学习估计器的渐近不可容忍性

arXiv:2606.22391v2 Announce Type: replace-cross Abstract: Structure-agnostic (SA) models introduced by Balakrishnan et al. (2026) aim to reflect the general lack of knowledge of structural assumptions on data-generating laws such as smoothness or sparsity in practice. Roughly spe…

本文如何被排名

报道来源 [1]

关于结构无关模型下双重机器学习估计器的渐近不可容忍性

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