Debiased Machine Learning U-statistics
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866915537061150720 |
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| author | Escanciano, Juan Carlos Terschuur, Joël Robert |
| author_facet | Escanciano, Juan Carlos Terschuur, Joël Robert |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2206_05235 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Debiased Machine Learning U-statistics Escanciano, Juan Carlos Terschuur, Joël Robert Econometrics 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. |
| title | Debiased Machine Learning U-statistics |
| topic | Econometrics |
| url | https://arxiv.org/abs/2206.05235 |