Debiased Machine Learning U-statistics

Juan Carlos Escanciano and Jo\"el Robert Terschuur

Papers from arXiv.org

Abstract: We propose a method to debias estimators based on U-statistics with Machine Learning (ML) first-steps. Standard plug-in estimators often suffer from regularization and model-selection biases, producing invalid inferences. We show that Debiased Machine Learning (DML) estimators can be constructed within a U-statistics framework to correct these biases while preserving desirable statistical properties. The approach delivers simple, robust estimators with provable asymptotic normality and good finite-sample performance. We apply our method to three problems: inference on Inequality of Opportunity (IOp) using the Gini coefficient of ML-predicted incomes given circumstances, inference on predictive accuracy via the Area Under the Curve (AUC), and inference on linear models with ML-based sample-selection corrections. Using European survey data, we present the first debiased estimates of income IOp. In our empirical application, commonly employed ML-based plug-in estimators systematically underestimate IOp, while our debiased estimators are robust across ML methods.

Date: 2022-06, Revised 2025-10
New Economics Papers: this item is included in nep-big, nep-cmp, nep-dem and nep-ecm
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2206.05235

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