S. Wager

Publications89
h-index22
Citations3,269
Highly Influential Citations461
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  • Publications
  • Influence
Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
TLDR
We develop a nonparametric causal forest for estimating heterogeneous treatment effects that extends Breiman’s widely used random forest algorithm. Expand
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Generalized Random Forests
We propose generalized random forests, a method for non-parametric statistical estimation based on random forests (Breiman, 2001) that can be used to fit any quantity of interest identified as the… Expand
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Approximate Residual Balancing: De-Biased Inference of Average Treatment Effects in High Dimensions.
There are many settings where researchers are interested in estimating average treatment effects and are willing to rely on the unconfoundedness assumption, which requires that the treatment… Expand
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Dropout Training as Adaptive Regularization
TLDR
We develop a natural semi-supervised algorithm that uses unlabeled data to create a better adaptive regularizer for generalized linear models, and show that it consistently boosts the performance of dropout training. Expand
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Efficient Policy Learning
TLDR
We propose a new approach to this problem motivated by the theory of semiparametrically efficient estimation, and establish strong guarantees for the asymptotic utilitarian regret of the resulting policy. Expand
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Quasi-Oracle Estimation of Heterogeneous Treatment Effects
TLDR
Flexible estimation of heterogeneous treatment effects lies at the heart of many statistical challenges, such as personalized medicine and optimal resource allocation. Expand
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Sequential selection procedures and false discovery rate control
Summary We consider a multiple-hypothesis testing setting where the hypotheses are ordered and one is only permitted to reject an initial contiguous block of hypotheses. A rejection rule in this… Expand
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Confidence intervals for random forests: the jackknife and the infinitesimal jackknife
TLDR
We study the variability of predictions made by bagged learners and random forests, and show how to estimate standard errors for these methods. Expand
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High-Dimensional Asymptotics of Prediction: Ridge Regression and Classification
We provide a unified analysis of the predictive risk of ridge regression and regularized discriminant analysis in a dense random effects model. We work in a high-dimensional asymptotic regime where… Expand
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Estimating Treatment Effects with Causal Forests: An Application
We apply causal forests to a dataset derived from the National Study of Learning Mindsets, and consider resulting practical and conceptual challenges. In particular, we discuss how causal forests use… Expand
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