
Optimal Model Selection in Contextual Bandits with Many Classes via Offline Oracles
We study the problem of model selection for contextual bandits, in which...
read it

OffPolicy Evaluation via Adaptive Weighting with Data from Contextual Bandits
It has become increasingly common for data to be collected adaptively, f...
read it

Policy Learning with Adaptively Collected Data
Learning optimal policies from historical data enables the gains from pe...
read it

Adapting to misspecification in contextual bandits with offline regression oracles
Computationally efficient contextual bandits are often based on estimati...
read it

Tractable contextual bandits beyond realizability
Tractable contextual bandit algorithms often rely on the realizability a...
read it

Combining Experimental and Observational Data to Estimate Treatment Effects on Long Term Outcomes
There has been an increase in interest in experimental evaluations to es...
read it

Survey Bandits with Regret Guarantees
We consider a variant of the contextual bandit problem. In standard cont...
read it

Stable Prediction with Model Misspecification and Agnostic Distribution Shift
For many machine learning algorithms, two main assumptions are required ...
read it

Optimal Experimental Design for Staggered Rollouts
Experimentation has become an increasingly prevalent tool for guiding po...
read it

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

Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulations
Researchers often use artificial data to assess the performance of new e...
read it

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

Counterfactual Inference for Consumer Choice Across Many Product Categories
This paper proposes a method for estimating consumer preferences among d...
read it

Machine Learning Methods Economists Should Know About
We discuss the relevance of the recent Machine Learning (ML) literature ...
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

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

Balanced Linear Contextual Bandits
Contextual bandit algorithms are sensitive to the estimation method of t...
read it

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

Designbased Analysis in DifferenceInDifferences Settings with Staggered Adoption
In this paper we study estimation of and inference for average treatment...
read it

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

Stable Prediction across Unknown Environments
In many important machine learning applications, the training distributi...
read it

Estimating Heterogeneous Consumer Preferences for Restaurants and Travel Time Using Mobile Location Data
This paper analyzes consumer choices over lunchtime restaurants using da...
read it

Estimation Considerations in Contextual Bandits
Contextual bandit algorithms seek to learn a personalized treatment assi...
read it

SHOPPER: A Probabilistic Model of Consumer Choice with Substitutes and Complements
We develop SHOPPER, a sequential probabilistic model of market baskets. ...
read it

Matrix Completion Methods for Causal Panel Data Models
In this paper we develop new methods for estimating causal effects in se...
read it

Structured Embedding Models for Grouped Data
Word embeddings are a powerful approach for analyzing language, and expo...
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

Estimating Treatment Effects using Multiple Surrogates: The Role of the Surrogate Score and the Surrogate Index
Estimating the longterm effects of treatments is of interest in many fi...
read it

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

Recursive Partitioning for Heterogeneous Causal Effects
In this paper we study the problems of estimating heterogeneity in causa...
read it
Susan Athey
verfied profile
Professor at Stanford Graduate School of Business
Faculty Director, Golub Capital Social Impact Lab