174 citations · 446 across the 34 of their papers we have counts for
40 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…
Continuously Updating GMM in Linear IV Models: A Polynomial Approach
Marcelo J. Moreira, Whitney K. Newey, Mahrad Sharifvaghefi
This paper characterizes the global minimum of the continuously updating generalized method of moments (CU-GMM) objective in linear instrumental variables models. We allow optimal…
Linear Estimation of Structural and Causal Effects for Nonseparable Panel Data
Victor Chernozhukov, Ben Deaner, Ying Gao +2
This paper develops linear estimators for structural and causal parameters of nonseparable models using panel data. These models incorporate unobserved, time-varying, individual he…
Higher-Order Neyman Orthogonality in Moment-Condition Models
Stéphane Bonhomme, Koen Jochmans, Whitney K. Newey +1
We construct moment functions that are Neyman-orthogonal to a chosen order in parametric moment condition models involving nuisance parameters. These moment functions reduce sensit…
Panel Estimation of Taxable Income Elasticities with Heterogeneity and Endogenous Budget Sets
Soren Blomquist, Anil Kumar, Whitney K. Newey
This paper introduces an estimator for the average of heterogeneous elasticities of taxable income (ETI), addressing key econometric challenges posed by nonlinear budget sets. Buil…
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…
Source Condition Double Robust Inference on Functionals of Inverse Problems
Andrew Bennett, Nathan Kallus, Xiaojie Mao +3
We consider estimation of parameters defined as linear functionals of solutions to linear inverse problems. Any such parameter admits a doubly robust representation that depends on…
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…
Minimax Instrumental Variable Regression and Convergence Guarantees without Identification or Closedness
Andrew Bennett, Nathan Kallus, Xiaojie Mao +3
In this paper, we study nonparametric estimation of instrumental variable (IV) regressions. Recently, many flexible machine learning methods have been developed for instrumental va…
Inference on Strongly Identified Functionals of Weakly Identified Functions
Andrew Bennett, Nathan Kallus, Xiaojie Mao +3
In a variety of applications, including nonparametric instrumental variable (NPIV) analysis, proximal causal inference under unmeasured confounding, and missing-not-at-random data…
Efficient Bias Correction for Cross-section and Panel Data
Jinyong Hahn, David W. Hughes, Guido Kuersteiner +1
Bias correction can often improve the finite sample performance of estimators. We show that the choice of bias correction method has no effect on the higher-order variance of semip…
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…
RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forests
Victor Chernozhukov, Whitney K. Newey, Victor Quintas-Martinez +1
Many causal and policy effects of interest are defined by linear functionals of high-dimensional or non-parametric regression functions. -consistent and asymptotically no…
A Simple and General Debiased Machine Learning Theorem with Finite Sample Guarantees
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 lea…
Automatic Debiased Machine Learning via Riesz Regression
Victor Chernozhukov, Whitney K. Newey, Victor Quintas-Martinez +1
A variety of interesting parameters may depend on high dimensional regressions. Machine learning can be used to estimate such parameters. However estimators based on machine learne…
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…
Heterogeneous Coefficients, Control Variables, and Identification of Multiple Treatment Effects
Whitney K. Newey, Sami Stouli
Multidimensional heterogeneity and endogeneity are important features of models with multiple treatments. We consider a heterogeneous coefficients model where the outcome is a line…
Minimax Semiparametric Learning With Approximate Sparsity
Jelena Bradic, Victor Chernozhukov, Whitney K. Newey +1
Estimating linear, mean-square continuous functionals is a pivotal challenge in statistics. In high-dimensional contexts, this estimation is often performed under the assumption of…
Testing the Drift-Diffusion Model
Drew Fudenberg, Whitney K. Newey, Philipp Strack +1
The drift diffusion model (DDM) is a model of sequential sampling with diffusion (Brownian) signals, where the decision maker accumulates evidence until the process hits a stopping…
Welfare Analysis in Dynamic Models
Victor Chernozhukov, Whitney Newey, Vira Semenova
This paper introduces metrics for welfare analysis in dynamic models. We develop estimation and inference for these parameters even in the presence of a high-dimensional state spac…
Identification of Treatment Effects under Limited Exogenous Variation
Whitney K. Newey, Sami Stouli
Multidimensional heterogeneity and endogeneity are important features of a wide class of econometric models. With control variables to correct for endogeneity, nonparametric identi…
Control Variables, Discrete Instruments, and Identification of Structural Functions
Whitney Newey, Sami Stouli
Control variables provide an important means of controlling for endogeneity in econometric models with nonseparable and/or multidimensional heterogeneity. We allow for discrete ins…
Automatic Debiased Machine Learning of Causal and Structural Effects
Victor Chernozhukov, Whitney K Newey, Rahul Singh
Many causal and structural effects depend on regressions. Examples include policy effects, average derivatives, regression decompositions, average treatment effects, causal mediati…