
Average Treatment Effects in the Presence of Interference
We propose a definition for the average indirect effect of a binary trea...
read it

Treatment Allocation under Uncertain Costs
We consider the problem of learning how to optimally allocate treatments...
read it

Diffusion Asymptotics for Sequential Experiments
We propose a new diffusionasymptotic analysis for sequentially randomiz...
read it

Random Graph Asymptotics for Treatment Effect Estimation under Network Interference
The network interference model for causal inference places all experimen...
read it

NoiseInduced Randomization in Regression Discontinuity Designs
Regression discontinuity designs are used to estimate causal effects in ...
read it

Estimating heterogeneous treatment effects with rightcensored data via causal survival forests
There is fastgrowing literature on estimating heterogeneous treatment e...
read it

Confidence Intervals for Policy Evaluation in Adaptive Experiments
Adaptive experiments can result in considerable cost savings in multiar...
read it

Doubly robust treatment effect estimation with missing attributes
Missing attributes are ubiquitous in causal inference, as they are in mo...
read it

SmoothnessAdaptive Stochastic Bandits
We consider the problem of nonparametric multiarmed bandits with stoch...
read it

CrossValidation, Risk Estimation, and Model Selection
Crossvalidation is a popular nonparametric method for evaluating the a...
read it

Sufficient Representations for Categorical Variables
Many learning algorithms require categorical data to be transformed into...
read it

CovariatePowered Empirical Bayes Estimation
We study methods for simultaneous analysis of many noisy experiments in ...
read it

Robust Nonparametric DifferenceinDifferences Estimation
We consider the problem of treatment effect estimation in differencein...
read it

Learning WhentoTreat Policies
Many applied decisionmaking problems have a dynamic component: The poli...
read it

Sparsity Double Robust Inference of Average Treatment Effects
Many popular methods for building confidence intervals on causal effects...
read it

Estimating Treatment Effects with Causal Forests: An Application
We apply causal forests to a dataset derived from the National Study of ...
read it

BiasAware Confidence Intervals for Empirical Bayes Analysis
In an empirical Bayes analysis, we use data from repeated sampling to im...
read it

Synthetic Difference in Differences
We present a new perspective on the Synthetic Control (SC) method as a w...
read it

Debiased Inference of Average Partial Effects in SingleIndex Models
We propose a method for average partial effect estimation in highdimens...
read it

Offline MultiAction Policy Learning: Generalization and Optimization
In many settings, a decisionmaker wishes to learn a rule, or policy, th...
read it

Local Linear Forests
Random forests are a powerful method for nonparametric regression, but ...
read it

Learning Objectives for Treatment Effect Estimation
We develop a general class of twostep algorithms for heterogeneous trea...
read it

Balancing Out Regression Error: Efficient Treatment Effect Estimation without Smooth Propensities
There has been a recent surge of interest in doubly robust approaches to...
read it

Efficient Policy Learning
We consider the problem of using observational data to learn treatment a...
read it

Generalized Random Forests
We propose generalized random forests, a method for nonparametric stati...
read it

Highdimensional regression adjustments in randomized experiments
We study the problem of treatment effect estimation in randomized experi...
read it

Data Augmentation via Levy Processes
If a document is about travel, we may expect that short snippets of the ...
read it

Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
Many scientific and engineering challenges  ranging from personalized ...
read it

Adaptive Concentration of Regression Trees, with Application to Random Forests
We study the convergence of the predictive surface of regression trees a...
read it

The Statistics of Streaming Sparse Regression
We present a sparse analogue to stochastic gradient descent that is guar...
read it

BootstrapBased Regularization for LowRank Matrix Estimation
We develop a flexible framework for lowrank matrix estimation that allo...
read it

Altitude Training: Strong Bounds for SingleLayer Dropout
Dropout training, originally designed for deep neural networks, has been...
read it

Asymptotic Theory for Random Forests
Random forests have proven to be reliable predictive algorithms in many ...
read it

Confidence Intervals for Random Forests: The Jackknife and the Infinitesimal Jackknife
We study the variability of predictions made by bagged learners and rand...
read it

Feedback Detection for Live Predictors
A predictor that is deployed in a live production system may perturb the...
read it

Weakly supervised clustering: Learning finegrained signals from coarse labels
Consider a classification problem where we do not have access to labels ...
read it

Dropout Training as Adaptive Regularization
Dropout and other feature noising schemes control overfitting by artific...
read it
Stefan Wager
is this you? claim profile
Assistant Professor of Operations, Information, and Technology, Assistant Professor of Statistics (by courtesy), School of Humanities and Sciences at Stanford University