Double/Debiased Machine Learning for Treatment and Structural Parameters
The paper develops a general framework for estimating low-dimensional treatment or structural parameters when high-dimensional nuisance components are estimated with machine learning methods. By combining Neyman-orthogonal (debiased) moment conditions with sample-splitting/cross-fitting, the approach removes regularization and overfitting biases. The resulting estimators are root-N consistent, asymptotically normal, and valid for inference despite slowly converging nuisance estimates.