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New statistical theory for debiased machine learning without cross-fitting

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This paper introduces a new statistical theory for debiased machine learning (DML) estimators in generalized method of moments (GMM) models. The proposed method allows for multiway clustered dependence without requiring cross-fitting, which can be inefficient and computationally intensive. The authors demonstrate that valid inference can be achieved by combining Neyman-orthogonal moment conditions with an empirical process approach, enabling asymptotic linearity and normality under multiway clustered dependence. A key technical contribution is the development of novel maximal inequalities for functions of sums of separately exchangeable arrays. AI

RANK_REASON Academic paper published on arXiv detailing new statistical theory for machine learning estimators. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv stat.ML →

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New statistical theory for debiased machine learning without cross-fitting

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Academic paper published on arXiv detailing new statistical theory for machine learning estimators. [lever_c_demoted from research: ic=1 ai=0.4]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Kaicheng Chen, Harold D. Chiang ·

    Cross-Fitting-Free Debiased Machine Learning with Multiway Dependence

    arXiv:2602.11333v3 Announce Type: replace-cross Abstract: This paper develops an asymptotic theory for two-step debiased machine learning (DML) estimators in generalised method of moments (GMM) models with general multiway clustered dependence, without relying on cross-fitting. W…

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