V
Victor Chernozhukov
Researcher at Massachusetts Institute of Technology
Publications - 374
Citations - 25283
Victor Chernozhukov is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Estimator & Quantile. The author has an hindex of 73, co-authored 370 publications receiving 20588 citations. Previous affiliations of Victor Chernozhukov include Amazon.com & New Economic School.
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Identification and Efficient Semiparametric Estimation of a Dynamic Discrete Game
TL;DR: In this article, the identification and estimation of a dynamic discrete game allowing for discrete or continuous state variables is studied and a general nonparametric identification result under the imposition of an exclusion restriction on agent payoffs is provided.
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Double machine learning for treatment and causal parameters
Victor Chernozhukov,Denis Chetverikov,Mert Demirer,Esther Duflo,Christian Hansen,Whitney K. Newey +5 more
TL;DR: In this paper, the authors proposed a double ML estimator, which combines auxiliary and main ML predictions to achieve the fastest rates of convergence and exhibit robust good behavior with respect to a broader class of probability distributions than naive "single" ML estimators.
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QRPROCESS: Stata module for quantile regression: fast algorithm, pointwise and uniform inference
TL;DR: The qrprocess as discussed by the authors package provides fast estimation and inference procedures for the linear quantile regression model, especially when a large number of quantile regressions or bootstrap replications must be estimated.
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DoubleML - An Object-Oriented Implementation of Double Machine Learning in Python.
TL;DR: DoubleML as mentioned in this paper is an open-source Python library implementing the double machine learning framework of Chernozhukov et al. It contains functionalities for valid statistical inference on causal parameters when the estimation of nuisance parameters is based on machine learning methods.
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Post-selection and post-regularization inference in linear models with many controls and instruments
TL;DR: In this article, the authors propose an approach to estimate structural parameters in the presence of many instruments and controls based on methods for estimating sparse high-dimensional models, which can be used to select both which instruments and which control variables to use.