723 citations · 5.4k across the 110 of their papers we have counts for
38 papers · 1 filter
Pragmatic DML with AI-Learned Representations
Andres Aradillas Fernandez, Victor Chernozhukov, Carlos Cinelli +5
Text, images, and other rich covariates are increasingly compressed into AI-learned representations and then used as controls in causal analysis. We study when this approach is val…
Automatic debiased machine learning and sensitivity analysis for sample selection models
Jakob Bjelac, Victor Chernozhukov, Phil-Adrian Klotz +2
In this paper, we extend the Riesz representation framework to causal inference under sample selection, where both treatment assignment and outcome observability are non-random. Fo…
Sensitivity Analysis for Causal ML: A Use Case at Booking.com
Philipp Bach, Victor Chernozhukov, Carlos Cinelli +4
Causal Machine Learning has emerged as a powerful tool for flexibly estimating causal effects from observational data in both industry and academia. However, causal inference from…
Agentic Economic Modeling
Bohan Zhang, Jiaxuan Li, Ali Hortaçsu +4
We introduce Agentic Economic Modeling (AEM), a framework that aligns synthetic LLM choices with small-sample human evidence for econometric inference. AEM first generates task-con…
Bivariate Distribution Regression; Theory, Estimation and an Application to Intergenerational Mobility
Victor Chernozhukov, Iván Fernández-Val, Jonas Meier +2
We employ distribution regression (DR) to estimate the joint distribution of two outcome variables conditional on chosen covariates. While Bivariate Distribution Regression (BDR) i…
Plausible GMM: A Quasi-Bayesian Approach
Victor Chernozhukov, Christian B. Hansen, Lingwei Kong +1
Structural estimation in economics often makes use of models formulated in terms of moment conditions. While these moment conditions are generally well-motivated, it is often unkno…
An Introduction to Double/Debiased Machine Learning
Achim Ahrens, Victor Chernozhukov, Christian Hansen +3
This paper provides an introduction to Double/Debiased Machine Learning (DML). DML is a general approach to performing inference about a target parameter in the presence of nuisanc…
Comment on "Sequential validation of treatment heterogeneity" and "Comment on generic machine learning inference on heterogeneous treatment effects in randomized experiments"
Victor Chernozhukov, Mert Demirer, Esther Duflo +1
We warmly thank Kosuke Imai, Michael Lingzhi Li, and Stefan Wager for their gracious and insightful comments. We are particularly encouraged that both pieces recognize the importan…
Policy Learning with Confidence
Victor Chernozhukov, Sokbae Lee, Adam M. Rosen +1
This paper introduces a rule for policy selection in the presence of estimation uncertainty, explicitly accounting for estimation risk. The rule belongs to the class of risk-aware…
Philip G. Wright, directed acyclic graphs, and instrumental variables
Jaap H. Abbring, Victor Chernozhukov, Iván Fernández-Val
Wright (1928) deals with demand and supply of oils and butter. In Appendix B of this book, Philip Wright made several fundamental contributions to causal inference. He introduced a…
Conditional Rank-Rank Regression
Victor Chernozhukov, Iván Fernández-Val, Jonas Meier +2
Rank-rank regression is commonly employed in economic research as a way of capturing the relationship between two economic variables. The slope of this regression is the Spearman r…
Applied Causal Inference Powered by ML and AI
Victor Chernozhukov, Christian Hansen, Nathan Kallus +2
An introduction to the emerging fusion of machine learning and causal inference. The book presents ideas from classical structural equation models (SEMs) and their modern AI equiva…
Estimating Causal Effects of Discrete and Continuous Treatments with Binary Instruments
Victor Chernozhukov, Iván Fernández-Val, Sukjin Han +1
We propose an instrumental variable framework for identifying and estimating causal effects of discrete and continuous treatments with binary instruments. The basis of our approach…
Hyperparameter Tuning for Causal Inference with Double Machine Learning: A Simulation Study
Philipp Bach, Oliver Schacht, Victor Chernozhukov +2
Proper hyperparameter tuning is essential for achieving optimal performance of modern machine learning (ML) methods in predictive tasks. While there is an extensive literature on t…
Arellano-Bond LASSO Estimator for Dynamic Linear Panel Models
Victor Chernozhukov, Iván Fernández-Val, Chen Huang +1
The Arellano-Bond estimator is a fundamental method for dynamic panel data models, widely used in practice. It can be severely biased when the time series dimension of the data, $T…
An MCMC Approach to Classical Estimation
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 distributi…
Automatic Debiased Machine Learning for Dynamic Treatment Effects and General Nested Functionals
Victor Chernozhukov, Whitney Newey, Rahul Singh +1
Many canonical models in causal inference and structural econometrics have recursive identification formulas. In causal inference, recursion arises when identification requires bot…
Long Story Short: Omitted Variable Bias in Causal Machine Learning
Victor Chernozhukov, Carlos Cinelli, Whitney Newey +2
We develop a general theory of omitted variable bias for a wide range of common causal parameters, including (but not limited to) averages of potential outcomes, average treatment…
Uniform Inference on High-dimensional Spatial Panel Networks
Victor Chernozhukov, Chen Huang, Weining Wang
We propose employing a high-dimensional generalized method of moments (GMM) estimator, regularized for dimension reduction and subsequently debiased to correct for shrinkage bias (…
Adversarial Estimation of Riesz Representers
Victor Chernozhukov, Whitney Newey, Rahul Singh +1
Many causal parameters are linear functionals of an underlying regression. The Riesz representer is a key component in the asymptotic variance of a semiparametrically estimated lin…
Instrumental Variable Quantile Regression
Victor Chernozhukov, Christian Hansen, Kaspar Wuthrich
This chapter reviews the instrumental variable quantile regression model of Chernozhukov and Hansen (2005). We discuss the key conditions used for identification of structural quan…
Distributional conformal prediction
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. Our…
Fast Algorithms for the Quantile Regression Process
Victor Chernozhukov, Iván Fernández-Val, Blaise Melly
The widespread use of quantile regression methods depends crucially on the existence of fast algorithms. Despite numerous algorithmic improvements, the computation time is still no…
SortedEffects: Sorted Causal Effects in R
Shuowen Chen, Victor Chernozhukov, Iván Fernández-Val +1
Chernozhukov et al. (2018) proposed the sorted effect method for nonlinear regression models. This method consists of reporting percentiles of the partial effects in addition to th…