
Adapting to misspecification in contextual bandits with offline regression oracles
Computationally efficient contextual bandits are often based on estimati...
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Tractable contextual bandits beyond realizability
Tractable contextual bandit algorithms often rely on the realizability a...
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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...
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Survey Bandits with Regret Guarantees
We consider a variant of the contextual bandit problem. In standard cont...
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Stable Prediction with Model Misspecification and Agnostic Distribution Shift
For many machine learning algorithms, two main assumptions are required ...
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Optimal Experimental Design for Staggered Rollouts
Experimentation has become an increasingly prevalent tool for guiding po...
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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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Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulations
Researchers often use artificial data to assess the performance of new e...
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Sufficient Representations for Categorical Variables
Many learning algorithms require categorical data to be transformed into...
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Counterfactual Inference for Consumer Choice Across Many Product Categories
This paper proposes a method for estimating consumer preferences among d...
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Machine Learning Methods Economists Should Know About
We discuss the relevance of the recent Machine Learning (ML) literature ...
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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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Synthetic Difference in Differences
We present a new perspective on the Synthetic Control (SC) method as a w...
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Balanced Linear Contextual Bandits
Contextual bandit algorithms are sensitive to the estimation method of t...
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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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Designbased Analysis in DifferenceInDifferences Settings with Staggered Adoption
In this paper we study estimation of and inference for average treatment...
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Local Linear Forests
Random forests are a powerful method for nonparametric regression, but ...
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Stable Prediction across Unknown Environments
In many important machine learning applications, the training distributi...
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Estimating Heterogeneous Consumer Preferences for Restaurants and Travel Time Using Mobile Location Data
This paper analyzes consumer choices over lunchtime restaurants using da...
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Estimation Considerations in Contextual Bandits
Contextual bandit algorithms seek to learn a personalized treatment assi...
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SHOPPER: A Probabilistic Model of Consumer Choice with Substitutes and Complements
We develop SHOPPER, a sequential probabilistic model of market baskets. ...
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Matrix Completion Methods for Causal Panel Data Models
In this paper we develop new methods for estimating causal effects in se...
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Structured Embedding Models for Grouped Data
Word embeddings are a powerful approach for analyzing language, and expo...
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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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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...
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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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Recursive Partitioning for Heterogeneous Causal Effects
In this paper we study the problems of estimating heterogeneity in causa...
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Susan Athey
verfied profile
Professor at Stanford Graduate School of Business
Faculty Director, Golub Capital Social Impact Lab