no code implementations • 9 Mar 2024 • Victor Chernozhukov, Iván Fernández-Val, Sukjin Han, Kaspar Wüthrich
This representation allows us to introduce an identifying assumption, so-called copula invariance, that restricts the local dependence of the copula with respect to the treatment propensity.
1 code implementation • 4 Mar 2024 • Victor Chernozhukov, Christian Hansen, Nathan Kallus, Martin Spindler, Vasilis Syrgkanis
An introduction to the emerging fusion of machine learning and causal inference.
no code implementations • 7 Feb 2024 • Philipp Bach, Oliver Schacht, Victor Chernozhukov, Sven Klaassen, Martin Spindler
First, we assess the importance of data splitting schemes for tuning ML learners within Double Machine Learning.
no code implementations • 1 Feb 2024 • Sven Klaassen, Jan Teichert-Kluge, Philipp Bach, Victor Chernozhukov, Martin Spindler, Suhas Vijaykumar
This paper explores the use of unstructured, multimodal data, namely text and images, in causal inference and treatment effect estimation.
1 code implementation • 1 Feb 2024 • Victor Chernozhukov, Iván Fernández-Val, Chen Huang, Weining Wang
However, the estimator is severely biased when the data's time series dimension $T$ is long due to the large degree of overidentification.
no code implementations • 28 Apr 2023 • Patrick Bajari, Zhihao Cen, Victor Chernozhukov, Manoj Manukonda, Suhas Vijaykunar, Jin Wang, Ramon Huerta, Junbo Li, Ling Leng, George Monokroussos, Shan Wan
To accomplish this, we generate abstract product attributes, or ``features,'' from text descriptions and images using deep neural networks, and then use these attributes to estimate the hedonic price function.
no code implementations • 18 Jan 2023 • Victor Chernozhukov, Han Hong
This paper studies computationally and theoretically attractive estimators called the Laplace type estimators (LTE), which include means and quantiles of Quasi-posterior distributions defined as transformations of general (non-likelihood-based) statistical criterion functions, such as those in GMM, nonlinear IV, empirical likelihood, and minimum distance methods.
1 code implementation • NeurIPS 2023 • Masatoshi Uehara, Haruka Kiyohara, Andrew Bennett, Victor Chernozhukov, Nan Jiang, Nathan Kallus, Chengchun Shi, Wen Sun
Finally, we extend our methods to learning of dynamics and establish the connection between our approach and the well-known spectral learning methods in POMDPs.
no code implementations • 25 Mar 2022 • Victor Chernozhukov, Whitney Newey, Rahul Singh, Vasilis Syrgkanis
We extend the idea of automated debiased machine learning to the dynamic treatment regime and more generally to nested functionals.
1 code implementation • 26 Dec 2021 • Victor Chernozhukov, Carlos Cinelli, Whitney Newey, Amit Sharma, Vasilis Syrgkanis
Therefore, simple plausibility judgments on the maximum explanatory power of omitted variables (in explaining treatment and outcome variation) are sufficient to place overall bounds on the size of the bias.
1 code implementation • 6 Oct 2021 • Victor Chernozhukov, Whitney K. Newey, Victor Quintas-Martinez, Vasilis Syrgkanis
We also propose a Random Forest method which learns a locally linear representation of the Riesz function.
no code implementations • 17 Jun 2021 • Gianluca Detommaso, Michael Brückner, Philip Schulz, Victor Chernozhukov
We extend the definition of the marginal causal effect to the continuous treatment setting and develop a novel characterization of causal bias in the framework of structural causal models.
no code implementations • 31 May 2021 • Victor Chernozhukov, Whitney K. Newey, Rahul Singh
Debiased machine learning is a meta algorithm based on bias correction and sample splitting to calculate confidence intervals for functionals, i. e. scalar summaries, of machine learning algorithms.
no code implementations • 16 May 2021 • Victor Chernozhukov, Chen Huang, Weining Wang
We propose employing a debiased-regularized, high-dimensional generalized method of moments (GMM) framework to perform inference on large-scale spatial panel networks.
1 code implementation • 10 May 2021 • Chengchun Shi, Runzhe Wan, Victor Chernozhukov, Rui Song
Off-policy evaluation learns a target policy's value with a historical dataset generated by a different behavior policy.
3 code implementations • 7 Apr 2021 • Philipp Bach, Victor Chernozhukov, Malte S. Kurz, Martin Spindler
DoubleML is an open-source Python library implementing the double machine learning framework of Chernozhukov et al. (2018) for a variety of causal models.
4 code implementations • 17 Mar 2021 • Philipp Bach, Victor Chernozhukov, Malte S. Kurz, Martin Spindler, Sven Klaassen
This paper serves as an introduction to the double machine learning framework and the R package DoubleML.
no code implementations • 25 Feb 2021 • Guillaume Carlier, Victor Chernozhukov, Gwendoline de Bie, Alfred Galichon
In this paper, we first revisit the Koenker and Bassett variational approach to (univariate) quantile regression, emphasizing its link with latent factor representations and correlation maximization problems.
1 code implementation • 20 Feb 2021 • Victor Chernozhukov, Hiroyuki Kasahara, Paul Schrimpf
This paper empirically examines how the opening of K-12 schools and colleges is associated with the spread of COVID-19 using county-level panel data in the United States.
no code implementations • 30 Dec 2020 • Victor Chernozhukov, Whitney Newey, Rahul Singh, Vasilis Syrgkanis
Many causal parameters are linear functionals of an underlying regression.
no code implementations • 17 Dec 2020 • Victor Chernozhukov, Denis Chetverikov, Yuta Koike
In this paper, we derive new, nearly optimal bounds for the Gaussian approximation to scaled averages of $n$ independent high-dimensional centered random vectors $X_1,\dots, X_n$ over the class of rectangles in the case when the covariance matrix of the scaled average is non-degenerate.
Probability Statistics Theory Statistics Theory 60F05, 62E17
no code implementations • 27 Dec 2019 • Jelena Bradic, Victor Chernozhukov, Whitney K. Newey, Yinchu Zhu
This paper is about the feasibility and means of root-n consistently estimating linear, mean-square continuous functionals of a high dimensional, approximately sparse regression.
1 code implementation • 17 Sep 2019 • Victor Chernozhukov, Kaspar Wüthrich, Yinchu Zhu
We propose a robust method for constructing conditionally valid prediction intervals based on models for conditional distributions such as quantile and distribution regression.
no code implementations • 24 Aug 2019 • Victor Chernozhukov, Whitney Newey, Vira Semenova
Second, we give a correction term for the transition density of the state variable.
1 code implementation • NeurIPS 2019 • Mert Demirer, Vasilis Syrgkanis, Greg Lewis, Victor Chernozhukov
Our results also apply if the model does not satisfy our semi-parametric form, but rather we measure regret in terms of the best projection of the true value function to this functional space.
1 code implementation • 27 Dec 2018 • Victor Chernozhukov, Kaspar Wuthrich, Yinchu Zhu
We propose a practical and robust method for making inferences on average treatment effects estimated by synthetic controls.
no code implementations • 11 Dec 2018 • Philipp Bach, Victor Chernozhukov, Martin Spindler
In 2016, the majority of full-time employed women in the U. S. earned significantly less than comparable men.
no code implementations • 28 Nov 2018 • Victor Chernozhukov, Iván Fernández-Val, Siyi Luo
We develop a distribution regression model under endogenous sample selection.
no code implementations • 13 Sep 2018 • Philipp Bach, Victor Chernozhukov, Martin Spindler
Due to the increasing availability of high-dimensional empirical applications in many research disciplines, valid simultaneous inference becomes more and more important.
no code implementations • 4 Sep 2018 • Xi Chen, Victor Chernozhukov, Iván Fernández-Val, Scott Kostyshak, Ye Luo
A common problem in econometrics, statistics, and machine learning is to estimate and make inference on functions that satisfy shape restrictions.
1 code implementation • 30 Aug 2018 • Sven Klaassen, Jannis Kück, Martin Spindler, Victor Chernozhukov
Graphical models have become a very popular tool for representing dependencies within a large set of variables and are key for representing causal structures.
no code implementations • 23 Feb 2018 • Victor Chernozhukov, Whitney Newey, Rahul Singh
To achieve this property, we include the Riesz representer for the functional as an additional nuisance parameter.
no code implementations • 17 Feb 2018 • Victor Chernozhukov, Kaspar Wuthrich, Yinchu Zhu
We extend conformal inference to general settings that allow for time series data.
no code implementations • 28 Dec 2017 • Vira Semenova, Matt Goldman, Victor Chernozhukov, Matt Taddy
The first step of our procedure is orthogonalization, where we partial out the controls and unit effects from the outcome and the base treatment and take the cross-fitted residuals.
3 code implementations • 25 Dec 2017 • Victor Chernozhukov, Kaspar Wüthrich, Yinchu Zhu
We introduce new inference procedures for counterfactual and synthetic control methods for policy evaluation.
2 code implementations • 13 Dec 2017 • Victor Chernozhukov, Mert Demirer, Esther Duflo, Iván Fernández-Val
We propose strategies to estimate and make inference on key features of heterogeneous effects in randomized experiments.
no code implementations • 27 Sep 2017 • Victor Chernozhukov, Alfred Galichon, Marc Henry, Brendan Pass
This paper derives conditions under which preferences and technology are nonparametrically identified in hedonic equilibrium models, where products are differentiated along more than one dimension and agents are characterized by several dimensions of unobserved heterogeneity.
no code implementations • 21 Feb 2017 • Vira Semenova, Victor Chernozhukov
This paper provides estimation and inference methods for the best linear predictor (approximation) of a structural function, such as conditional average structural and treatment effects, and structural derivatives, based on modern machine learning (ML) tools.
no code implementations • 30 Jan 2017 • Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey
A more general discussion and references to the existing literature are available in Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, and Newey (2016).
1 code implementation • 18 Aug 2016 • Victor Chernozhukov, Iván Fernández-Val, Blaise Melly, Kaspar Wüthrich
In both applications, the outcomes of interest are discrete rendering existing inference methods invalid for obtaining uniform confidence bands for quantile and quantile effects functions.
Methodology Econometrics 62F25, 62G15, 62P20
no code implementations • 1 Aug 2016 • Victor Chernozhukov, Chris Hansen, Martin Spindler
In this article the package High-dimensional Metrics (\texttt{hdm}) is introduced.
4 code implementations • 30 Jul 2016 • Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey, James Robins
Fortunately, this regularization bias can be removed by solving auxiliary prediction problems via ML tools.
4 code implementations • 5 Mar 2016 • Victor Chernozhukov, Chris Hansen, Martin Spindler
The package High-dimensional Metrics (\Rpackage{hdm}) is an evolving collection of statistical methods for estimation and quantification of uncertainty in high-dimensional approximately sparse models.
1 code implementation • 17 Dec 2015 • Victor Chernozhukov, Ivan Fernandez-Val, Ye Luo
They are as convenient and easy to report in practice as the conventional average partial effects.
Methodology Econometrics
1 code implementation • 18 Jun 2014 • Guillaume Carlier, Victor Chernozhukov, Alfred Galichon
Under correct specification, the notion produces strong representation, $Y=\beta \left(U\right) ^\top f(Z)$, for $f(Z)$ denoting a known set of transformations of $Z$, where $u \longmapsto \beta(u)^\top f(Z)$ is a monotone map, the gradient of a convex function, and the quantile regression coefficients $u \longmapsto \beta(u)$ have the interpretations analogous to that of the standard scalar quantile regression.
Methodology 49Q20, 49Q10, 90B20
no code implementations • 11 Nov 2013 • Alexandre Belloni, Victor Chernozhukov, Ivan Fernández-Val, Christian Hansen
In the latter case, our approach produces efficient estimators and honest bands for (functional) average treatment effects (ATE) and quantile treatment effects (QTE).