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On the Asymptotic Properties of Debiased Machine Learning Estimators

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  • Amilcar Velez

Abstract

This paper studies the properties of debiased machine learning (DML) estimators under a novel asymptotic framework, offering insights for improving the performance of these estimators in applications. DML is an estimation method suited to economic models where the parameter of interest depends on unknown nuisance functions that must be estimated. It requires weaker conditions than previous methods while still ensuring standard asymptotic properties. Existing theoretical results do not distinguish between two alternative versions of DML estimators, DML1 and DML2. Under a new asymptotic framework, this paper demonstrates that DML2 asymptotically dominates DML1 in terms of bias and mean squared error, formalizing a previous conjecture based on simulation results regarding their relative performance. Additionally, this paper provides guidance for improving the performance of DML2 in applications.

Suggested Citation

  • Amilcar Velez, 2024. "On the Asymptotic Properties of Debiased Machine Learning Estimators," Papers 2411.01864, arXiv.org.
    • Handle: RePEc:arx:papers:2411.01864
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    References listed on IDEAS

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    Cited by:

    1. Achim Ahrens & Victor Chernozhukov & Christian Hansen & Damian Kozbur & Mark Schaffer & Thomas Wiemann, 2025. "An Introduction to Double/Debiased Machine Learning," Papers 2504.08324, arXiv.org, revised Feb 2026.
    2. Bruno Fava, 2025. "Training and Testing with Multiple Splits: A Central Limit Theorem for Split-Sample Estimators," Papers 2511.04957, arXiv.org, revised Nov 2025.
    3. Ben Deaner & Chen-Wei Hsiang & Andrei Zeleneev, 2025. "Inferring Treatment Effects in Large Panels by Uncovering Latent Similarities," Papers 2503.20769, arXiv.org, revised Mar 2025.

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