723 citations · 5.4k across the 110 of their papers we have counts for
131 papers
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…
Omitted Variable Bias in Difference-in-Differences Designs
Juejue Wang, Pedro H. C. Sant'Anna, Victor Chernozhukov +1
We study the omitted variable bias (OVB) problem in canonical difference-in-differences (DiD) designs when unobserved confounding induces departures from the parallel trends assump…
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…
Adventures in Demand Analysis Using AI
Philipp Bach, Victor Chernozhukov, Sven Klaassen +3
This paper advances empirical demand analysis by integrating multimodal product representations derived from artificial intelligence (AI). Using a detailed dataset of toy cars on t…
Conditional Influence Functions
Victor Chernozhukov, Whitney K. Newey, Vasilis Syrgkanis
There are many nonparametric objects of interest that are a function of a conditional distribution. One important example is an average treatment effect conditional on a subset of…
Automatic Doubly Robust Forests
Zhaomeng Chen, Junting Duan, Victor Chernozhukov +1
This paper proposes the automatic Doubly Robust Random Forest (DRRF) algorithm for estimating the conditional expectation of a moment functional in the presence of high-dimensional…
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…
DoubleMLDeep: Estimation of Causal Effects with Multimodal Data
Sven Klaassen, Jan Teichert-Kluge, Philipp Bach +3
This paper explores the use of unstructured, multimodal data, namely text and images, in causal inference and treatment effect estimation. We propose a neural network architecture…
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…
Automatic Debiased Machine Learning for Covariate Shifts
Victor Chernozhukov, Michael Newey, Whitney K Newey +2
We present machine learning estimators for causal and predictive parameters under covariate shift, where covariate distributions differ between training and target populations. One…
Hedonic Prices and Quality Adjusted Price Indices Powered by AI
Patrick Bajari, Zhihao Cen, Victor Chernozhukov +8
We develop empirical models that efficiently process large amounts of unstructured product data (text, images, prices, quantities) to produce accurate hedonic price estimates and d…
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…
Future-Dependent Value-Based Off-Policy Evaluation in POMDPs
Masatoshi Uehara, Haruka Kiyohara, Andrew Bennett +5
We study off-policy evaluation (OPE) for partially observable MDPs (POMDPs) with general function approximation. Existing methods such as sequential importance sampling estimators…