Juan Carlos Escanciano, Joël Robert Terschuur
arXiv 10 Jun 2022 · Econometrics · 2 citations (OpenAlex)
arXiv:2206.05235 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Ahn, H. and J. L. Powell (1993) Semiparametric estimation of censored selection models with a nonparametric selection mechanism | 1.000 | 14 | 3 | 100% |
| 2 | Chernozhukov, V., D. Chetverikov, M. Demirer, E. Duflo, C. Hansen, W… (2018) Double/debiased machine learning for treatment and structural parameters | 0.874 | 8 | 2 | 100% |
| 3 | Brunori, P., P. Hufe, and D. Mahler (2021) The roots of inequality: Estimating inequality of opportunity from regression trees and forests | 0.843 | 3 | 3 | 100% |
| 4 | Bradley, A. P (1997) The use of the area under the ROC curve in the evaluation of machine learning algorithms | 0.811 | 4 | 2 | 100% |
| 5 | Newey, W. K (1994) "The asymptotic variance of semiparametric estimators," | 0.794 | 8 | 3 | 50% |
| 6 | Brunori, P., F. Palmisano, and V. Peragine (2019) a): Inequality of opportunity in sub-Saharan Africa | 0.644 | 2 | 2 | 100% |
| 7 | Brunori, P. and G. Neidhöfer (2021) The evolution of inequality of opportunity in Germany: A machine learning approach | 0.644 | 2 | 2 | 100% |
| 8 | Chernozhukov, V., J. C. Escanciano, H. Ichimura, W. K. Newey, and J.… (2022) Locally robust semiparametric estimation self | 0.644 | 2 | 2 | 100% |
| 9 | Ferreira, F. H. and J. Gignoux (2011) The measurement of inequality of opportunity: Theory and an application to Latin America | 0.644 | 2 | 2 | 100% |
| 10 | Honoré, B. and J. Powell (2005) Pairwise difference estimators for nonlinear models, in | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 94 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Cross-Fitting-Free Debiased Machine Learning with Multiway Dependence | 0.405 | 1 | 1 |