Victor Chernozhukov

TL;DR: In this article, the authors show that the impact of regularization bias and overfitting on estimation of the parameter of interest θ0 can be removed by using two simple, yet critical, ingredients: (1) using Neyman-orthogonal moments/scores that have reduced sensitivity with respect to nuisance parameters, and (2) making use of cross-fitting, which provides an efficient form of data-splitting.

TL;DR: In this article, the authors developed a model of quantile treatment effects (QTE) in the presence of endogeneity and obtained conditions for identification of the QTE without functional form assumptions.

TL;DR: The authors proposed robust methods for inference about the effect of a treatment variable on a scalar outcome in the presence of very many regressors in a model with possibly non-Gaussian and heteroscedastic disturbances.

TL;DR: In this paper, preliminary results of this paper were presented at Chernozhukov's invited Cowles Foundation lecture at the Northern American meetings of the Econometric society in June of 2009.

TL;DR: The authors developed a framework for performing estimation and inference in econometric models with partial identification, focusing particularly on models characterized by moment inequalities and equalities, and developed methods for analyzing the asymptotic properties of sample criterion functions under set identification.