
Diffusion Asymptotics for Sequential Experiments
We propose a new diffusionasymptotic analysis for sequentially randomiz...
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Random Graph Asymptotics for Treatment Effect Estimation under Network Interference
The network interference model for causal inference places all experimen...
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NoiseInduced Randomization in Regression Discontinuity Designs
Regression discontinuity designs are used to estimate causal effects in ...
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Estimating heterogeneous treatment effects with rightcensored data via causal survival forests
There is fastgrowing literature on estimating heterogeneous treatment e...
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Confidence Intervals for Policy Evaluation in Adaptive Experiments
Adaptive experiments can result in considerable cost savings in multiar...
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Doubly robust treatment effect estimation with missing attributes
Missing attributes are ubiquitous in causal inference, as they are in mo...
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SmoothnessAdaptive Stochastic Bandits
We consider the problem of nonparametric multiarmed bandits with stoch...
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CrossValidation, Risk Estimation, and Model Selection
Crossvalidation is a popular nonparametric method for evaluating the a...
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Sufficient Representations for Categorical Variables
Many learning algorithms require categorical data to be transformed into...
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CovariatePowered Empirical Bayes Estimation
We study methods for simultaneous analysis of many noisy experiments in ...
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Robust Nonparametric DifferenceinDifferences Estimation
We consider the problem of treatment effect estimation in differencein...
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Learning WhentoTreat Policies
Many applied decisionmaking problems have a dynamic component: The poli...
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Sparsity Double Robust Inference of Average Treatment Effects
Many popular methods for building confidence intervals on causal effects...
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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 ...
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BiasAware Confidence Intervals for Empirical Bayes Analysis
In an empirical Bayes analysis, we use data from repeated sampling to im...
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Synthetic Difference in Differences
We present a new perspective on the Synthetic Control (SC) method as a w...
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Debiased Inference of Average Partial Effects in SingleIndex Models
We propose a method for average partial effect estimation in highdimens...
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Offline MultiAction Policy Learning: Generalization and Optimization
In many settings, a decisionmaker wishes to learn a rule, or policy, th...
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Local Linear Forests
Random forests are a powerful method for nonparametric regression, but ...
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Learning Objectives for Treatment Effect Estimation
We develop a general class of twostep algorithms for heterogeneous trea...
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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...
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Efficient Policy Learning
We consider the problem of using observational data to learn treatment a...
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Generalized Random Forests
We propose generalized random forests, a method for nonparametric stati...
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Highdimensional regression adjustments in randomized experiments
We study the problem of treatment effect estimation in randomized experi...
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Data Augmentation via Levy Processes
If a document is about travel, we may expect that short snippets of the ...
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Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
Many scientific and engineering challenges  ranging from personalized ...
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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...
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The Statistics of Streaming Sparse Regression
We present a sparse analogue to stochastic gradient descent that is guar...
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BootstrapBased Regularization for LowRank Matrix Estimation
We develop a flexible framework for lowrank matrix estimation that allo...
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Altitude Training: Strong Bounds for SingleLayer Dropout
Dropout training, originally designed for deep neural networks, has been...
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Asymptotic Theory for Random Forests
Random forests have proven to be reliable predictive algorithms in many ...
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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...
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Feedback Detection for Live Predictors
A predictor that is deployed in a live production system may perturb the...
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Weakly supervised clustering: Learning finegrained signals from coarse labels
Consider a classification problem where we do not have access to labels ...
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Dropout Training as Adaptive Regularization
Dropout and other feature noising schemes control overfitting by artific...
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Stefan Wager
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Assistant Professor of Operations, Information, and Technology, Assistant Professor of Statistics (by courtesy), School of Humanities and Sciences at Stanford University