Victor Chernozhukov

TL;DR: In this paper, the authors proposed new methods for estimating and constructing confidence regions for a regression parameter of primary interest, a parameter in front of the regressor of interest, such as the treatment variable or a policy variable.

TL;DR: This analysis provides a set of high-level conditions under which inference for the low-dimensional parameter based on testing or point estimation methods will be regular despite selection or regularization biases occurring in the estimation of the high-dimensional nuisance parameter.

TL;DR: In this article, a censored quantile instrumental variable (CQIV) estimator is proposed to deal semiparametrically with censoring, with a control variable approach to incorporate endogenous regressors.

TL;DR: New inference methods for counterfactual and synthetic control methods for evaluating policy effects are introduced and the causal effect of election day registration (EDR) laws on voter turnout in the United States is re-evaluate.

TL;DR: A self-tuning Lasso method that simultaneously resolves three important practical problems in high-dimensional regression analysis, namely it handles the unknown scale, heteroscedasticity and (drastic) non-Gaussianity of the noise, and generates sharp bounds even in extreme cases, such as the infinite variance case and the noiseless case.