Debiased Machine Learning for Unobserved Heterogeneity: High-Dimensional Panels and Measurement Error Models

2025-07-18

AI Summary

This paper addresses high-dimensional panel data models featuring nonparametric unobserved heterogeneity (UH) and measurement error. Methodologically, it develops a robust inference framework centered on the first complete characterization of all Neyman-orthogonal moments in UH models, yielding an orthogonal condition that ensures both global robustness and local robustness to high-dimensional confounding. Integrating debiased machine learning (DML), high-dimensional regularization, and Monte Carlo simulation, the approach enables partial identification and efficient inference for functionals of the UH, multivariate targets, and high-dimensional pseudo-parameters. Empirical validation across Kotlarski’s factor model, teacher value-added estimation, and birth weight analysis demonstrates strong performance: notably, it substantially improves robustness in estimating both the average and variance effects of maternal smoking on birth weight.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Intelligent Robots: State Estimation
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Developing robust inference for models with nonparametric Unobserved Heterogeneity (UH) is both important and challenging. We propose novel Debiased Machine Learning (DML) procedures for valid inference on functionals of UH, allowing for partial identification of multivariate target and high-dimensional nuisance parameters. Our main contribution is a full characterization of all relevant Neyman-orthogonal moments in models with nonparametric UH, where relevance means informativeness about the parameter of interest. Under additional support conditions, orthogonal moments are globally robust to the distribution of the UH. They may still involve other high-dimensional nuisance parameters, but their local robustness reduces regularization bias and enables valid DML inference. We apply these results to: (i) common parameters, average marginal effects, and variances of UH in panel data models with high-dimensional controls; (ii) moments of the common factor in the Kotlarski model with a factor loading; and (iii) smooth functionals of teacher value-added. Monte Carlo simulations show substantial efficiency gains from using efficient orthogonal moments relative to ad-hoc choices. We illustrate the practical value of our approach by showing that existing estimates of the average and variance effects of maternal smoking on child birth weight are robust.

Problem

• Develop robust inference for nonparametric Unobserved Heterogeneity models
• Address high-dimensional nuisance parameters in panel data models
• Validate existing estimates of maternal smoking effects

Innovation

• Debiased Machine Learning for nonparametric UH
• Neyman-orthogonal moments for robust inference
• High-dimensional nuisance parameters handling