Debiased/Double Machine Learning (DML)

• Debiased/Double Machine Learning is a framework that estimates low-dimensional parameters using Neyman-orthogonal moment functions and K-fold cross-fitting for robust statistical inference.
• It leverages flexible machine learning models for nuisance estimation while preserving root-n consistency and asymptotic normality.
• DML is practically applied in econometric analyses, such as estimating average treatment effects and structural coefficient parameters.

Double/Debiased Machine Learning (DML) is a general framework for the construction of estimators for low-dimensional parameters of interest—such as average treatment effects (ATE) or coefficients in structural econometric models—in the presence of high-dimensional or complex nuisance functions. DML achieves valid inference by combining Neyman-orthogonal moment functions and K-fold cross-fitting, allowing arbitrary flexible ML methods for nuisance estimation while preserving root-n consistency and asymptotic normality for the parameter of interest. The methodology