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New research questions Double Machine Learning estimator admissibility

By PulseAugur Editorial · [1 sources] ·

A new paper by Lin Liu and colleagues challenges the asymptotic inadmissibility of Double Machine Learning (DML) estimators within structure-agnostic (SA) models. While DML estimators were previously shown to be minimax for certain functionals under SA models, this research demonstrates that for two specific functionals, DML estimators are asymptotically inadmissible. The paper introduces second-order estimators, specifically empirical higher-order influence function (HOIF) estimators, which asymptotically dominate the DML estimators in these cases. AI

IMPACT This research contributes to the theoretical understanding of estimator performance in machine learning, potentially influencing the development of more robust statistical methods.

RANK_REASON Academic paper detailing theoretical findings in statistical machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New research questions Double Machine Learning estimator admissibility

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Academic paper detailing theoretical findings in statistical machine learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Lin Liu, Rajarshi Mukherjee, James M Robins ·

    On the Asymptotic Inadmissibility of Double Machine Learning Estimators Under Structure-Agnostic Models

    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…

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