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

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-dimensi