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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Shape-enforcing operators for point and interval estimators
TL;DR: In this article, a method to enforce shape restrictions on point and interval estimates of the target function by applying functional operators is proposed, and the shape-enforced point estimates are closer to the target functions than the original point estimates.
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Generic Machine Learning Inference on Heterogenous Treatment Effects in Randomized Experiments
TL;DR: This article proposed a variational approach to estimate and make inference on key features of heterogeneous effects in randomized experiments, such as best linear predictors of the effects using machine learning proxies, average effects sorted by impact groups, and average characteristics of most and least impacted units.
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The sorted effects method: discovering heterogeneous effects beyond their averages
TL;DR: In this article, a collection of estimated partial e?ects are sorted in increasing order and indexed by percentiles, and a quantification of uncertainty (standard errors and con?dence bands) is provided.
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Double/Debiased Machine Learning for Treatment and Causal Parameters
Victor Chernozhukov,Denis Chetverikov,Mert Demirer,Esther Duflo,Christian Hansen,Whitney K. Newey,James Robins +6 more
TL;DR: In this article, the authors proposed a double ML method, which combines auxiliary and main ML predictions to build a de-biased estimator of the target parameter which typically will converge at the fastest possible 1/root(n) rate and be approximately unbiased and normal.
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Semiparametric Estimation of Structural Functions in Nonseparable Triangular Models
TL;DR: In this article, the authors introduce two classes of semiparametric non-separable triangular models based on distribution and quantile regression modeling of the reduced form conditional distributions of the endogenous variables.