[Statlist] Research Seminar in Statistics | *FRIDAY, DECEMBER 12, 2025* | GSEM, University of Geneva

[gsem-support-instituts]

Dear all,

We are pleased to invite you to our next Research Seminar, organized by Professor Davide La Vecchia on behalf of the Research Institute for Statistics and Information Science.
< https://www.unige.ch/gsem/en/research/institutes/risis/team/ >

FRIDAY, DECEMBER 12, 2025, at 11:15 am, Uni Mail M 5220

Debiased Machine Learning U-Statistics  
Juan Carlos ESCANCIANO REYERO, Universidad Carlos III de Madrid, Spain 
< https://economics.uc3m.es/personal/juan-carlos-escanciano/ >

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.

(jointly with Joël R. Terschuur)

> View the Research Seminar agenda: < https://www.unige.ch/gsem/en/research/seminars/risis/ >

With kind regards,

Birgit Müller-Marreros 
Assistant to the Research Institutes
gsem-support-instituts at unige.ch

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