1 citations · 1 across the 4 of their papers we have counts for
4 papers
Automatic Debiased Machine Learning of Structural Parameters with General Conditional Moments
Facundo Argañaraz
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)…
Debiased Machine Learning for Unobserved Heterogeneity: High-Dimensional Panels and Measurement Error Models
Facundo Argañaraz, Juan Carlos Escanciano
Developing robust inference for models with nonparametric Unobserved Heterogeneity (UH) is both important and challenging. We propose novel Debiased Machine Learning (DML) procedur…
Machine Learning Debiasing with Conditional Moment Restrictions: An Application to LATE
Facundo Argañaraz, Juan Carlos Escanciano
Models with Conditional Moment Restrictions (CMRs) are popular in economics. These models involve finite and infinite dimensional parameters. The infinite dimensional components in…
When Is Neyman-Orthogonal Inference Feasible? Existence and Relevance
Facundo Argañaraz, Juan Carlos Escanciano
Neyman-orthogonal moments underpin debiased and double machine learning, yet their existence and informativeness are typically presumed. We ask three questions: when do nontrivial…