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

Random Graph Asymptotics for Treatment Effect Estimation under Network Interference

The network interference model for causal inference places all experimen...
07/27/2020 ∙ by Shuangning Li, et al. ∙ 0 ∙ share

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Noise-Induced Randomization in Regression Discontinuity Designs

Regression discontinuity designs are used to estimate causal effects in ...
04/20/2020 ∙ by Dean Eckles, et al. ∙ 0 ∙ 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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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Stefan Wager

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