106 citations · 636 across the 106 of their papers we have counts for
6 papers · 1 filter
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 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…
Asymptotics of the Empirical Bootstrap Method Beyond Asymptotic Normality
Morgane Austern, Vasilis Syrgkanis
One of the most commonly used methods for forming confidence intervals for statistical inference is the empirical bootstrap, which is especially expedient when the limiting distrib…
Estimation and Inference with Trees and Forests in High Dimensions
Vasilis Syrgkanis, Manolis Zampetakis
We analyze the finite sample mean squared error (MSE) performance of regression trees and forests in the high dimensional regime with binary features, under a sparsity constraint.…
Orthogonal Statistical Learning
Dylan J. Foster, Vasilis Syrgkanis
We provide non-asymptotic excess risk guarantees for statistical learning in a setting where the population risk with respect to which we evaluate the target parameter depends on a…
Regularized Orthogonal Machine Learning for Nonlinear Semiparametric Models
Denis Nekipelov, Vira Semenova, Vasilis Syrgkanis
This paper proposes a Lasso-type estimator for a high-dimensional sparse parameter identified by a single index conditional moment restriction (CMR). In addition to this parameter,…