Automatic Debiased Machine Learning of Structural Parameters with General Conditional Moments

Understand

This paper proposes a method to automatically construct or estimate Neyman-orthogonal moments in general models defined by a finite number of conditional moment restrictions (CMRs), with possibly different conditioning variables and endogenous regressors.

• CMRs are allowed to depend on non-parametric components, which might be flexibly modeled using Machine Learning tools, and non-linearly on finite-dimensional parameters.
• The key step in this construction is the estimation of Orthogonal Instrumental Variables (OR-IVs) -- "residualized" functions of the conditioning variables, which are then combined to obtain a debiased moment.
• We argue that computing OR-IVs necessarily requires solving potentially complicated functional equations, which depend on unknown terms.