Stefan Wager

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Google ∙ Stanford University

Assistant Professor of Operations, Information, and Technology, Assistant Professor of Statistics (by courtesy), School of Humanities and Sciences at Stanford University

Local Linear Forests

Random forests are a powerful method for non-parametric regression, but ...
07/30/2018 ∙ by Rina Friedberg, et al. ∙ 52 ∙ share

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Sufficient Representations for Categorical Variables

Many learning algorithms require categorical data to be transformed into...
08/26/2019 ∙ by Jonathan Johannemann, et al. ∙ 25 ∙ share

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Confidence Intervals for Policy Evaluation in Adaptive Experiments

Adaptive experiments can result in considerable cost savings in multi-ar...
11/07/2019 ∙ by Vitor Hadad, et al. ∙ 14 ∙ share

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Offline Multi-Action Policy Learning: Generalization and Optimization

In many settings, a decision-maker wishes to learn a rule, or policy, th...
10/10/2018 ∙ by Zhengyuan Zhou, et al. ∙ 4 ∙ share

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Estimating heterogeneous treatment effects with right-censored data via causal survival forests

There is fast-growing literature on estimating heterogeneous treatment e...
01/27/2020 ∙ by Yifan Cui, et al. ∙ 1 ∙ share

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Efficient Policy Learning

We consider the problem of using observational data to learn treatment a...
02/09/2017 ∙ by Susan Athey, et al. ∙ 0 ∙ share

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Generalized Random Forests

We propose generalized random forests, a method for non-parametric stati...
10/05/2016 ∙ by Susan Athey, et al. ∙ 0 ∙ share

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High-dimensional regression adjustments in randomized experiments

We study the problem of treatment effect estimation in randomized experi...
07/22/2016 ∙ by Stefan Wager, et al. ∙ 0 ∙ share

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Data Augmentation via Levy Processes

If a document is about travel, we may expect that short snippets of the ...
03/21/2016 ∙ by Stefan Wager, et al. ∙ 0 ∙ share

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Estimation and Inference of Heterogeneous Treatment Effects using Random Forests

Many scientific and engineering challenges -- ranging from personalized ...
10/14/2015 ∙ by Stefan Wager, et al. ∙ 0 ∙ share

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Adaptive Concentration of Regression Trees, with Application to Random Forests

We study the convergence of the predictive surface of regression trees a...
03/22/2015 ∙ by Stefan Wager, et al. ∙ 0 ∙ share

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The Statistics of Streaming Sparse Regression

We present a sparse analogue to stochastic gradient descent that is guar...
12/13/2014 ∙ by Jacob Steinhardt, et al. ∙ 0 ∙ share

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Bootstrap-Based Regularization for Low-Rank Matrix Estimation

We develop a flexible framework for low-rank matrix estimation that allo...
10/30/2014 ∙ by Julie Josse, et al. ∙ 0 ∙ share

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Altitude Training: Strong Bounds for Single-Layer Dropout

Dropout training, originally designed for deep neural networks, has been...
07/11/2014 ∙ by Stefan Wager, et al. ∙ 0 ∙ share

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Asymptotic Theory for Random Forests

Random forests have proven to be reliable predictive algorithms in many ...
05/02/2014 ∙ by Stefan Wager, et al. ∙ 0 ∙ share

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Confidence Intervals for Random Forests: The Jackknife and the Infinitesimal Jackknife

We study the variability of predictions made by bagged learners and rand...
11/18/2013 ∙ by Stefan Wager, et al. ∙ 0 ∙ share

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Feedback Detection for Live Predictors

A predictor that is deployed in a live production system may perturb the...
10/10/2013 ∙ by Stefan Wager, et al. ∙ 0 ∙ share

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Weakly supervised clustering: Learning fine-grained signals from coarse labels

Consider a classification problem where we do not have access to labels ...
10/04/2013 ∙ by Stefan Wager, et al. ∙ 0 ∙ share

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Dropout Training as Adaptive Regularization

Dropout and other feature noising schemes control overfitting by artific...
07/04/2013 ∙ by Stefan Wager, et al. ∙ 0 ∙ share

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Balancing Out Regression Error: Efficient Treatment Effect Estimation without Smooth Propensities

There has been a recent surge of interest in doubly robust approaches to...
11/30/2017 ∙ by David A. Hirshberg, et al. ∙ 0 ∙ share

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Learning Objectives for Treatment Effect Estimation

We develop a general class of two-step algorithms for heterogeneous trea...
12/13/2017 ∙ by Xinkun Nie, et al. ∙ 0 ∙ share

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Debiased Inference of Average Partial Effects in Single-Index Models

We propose a method for average partial effect estimation in high-dimens...
11/06/2018 ∙ by David A. Hirshberg, et al. ∙ 0 ∙ share

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Synthetic Difference in Differences

We present a new perspective on the Synthetic Control (SC) method as a w...
12/24/2018 ∙ by Dmitry Arkhangelsky, et al. ∙ 0 ∙ share

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Bias-Aware Confidence Intervals for Empirical Bayes Analysis

In an empirical Bayes analysis, we use data from repeated sampling to im...
02/07/2019 ∙ by Nikolaos Ignatiadis, et al. ∙ 0 ∙ share

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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 ...
02/20/2019 ∙ by Susan Athey, et al. ∙ 0 ∙ share

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Learning When-to-Treat Policies

Many applied decision-making problems have a dynamic component: The poli...
05/23/2019 ∙ by Xinkun Nie, et al. ∙ 0 ∙ share

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Sparsity Double Robust Inference of Average Treatment Effects

Many popular methods for building confidence intervals on causal effects...
05/02/2019 ∙ by Jelena Bradic, et al. ∙ 0 ∙ share

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Robust Nonparametric Difference-in-Differences Estimation

We consider the problem of treatment effect estimation in difference-in-...
05/28/2019 ∙ by Chen Lu, et al. ∙ 0 ∙ share

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Covariate-Powered Empirical Bayes Estimation

We study methods for simultaneous analysis of many noisy experiments in ...
06/04/2019 ∙ by Nikolaos Ignatiadis, et al. ∙ 0 ∙ share

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Smoothness-Adaptive Stochastic Bandits

We consider the problem of non-parametric multi-armed bandits with stoch...
10/22/2019 ∙ by Yonatan Gur, et al. ∙ 0 ∙ share

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Cross-Validation, Risk Estimation, and Model Selection

Cross-validation is a popular non-parametric method for evaluating the a...
09/25/2019 ∙ by Stefan Wager, et al. ∙ 0 ∙ share

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Doubly robust treatment effect estimation with missing attributes

Missing attributes are ubiquitous in causal inference, as they are in mo...
10/23/2019 ∙ by Imke Mayer, et al. ∙ 0 ∙ share

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Stefan Wager

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