1.6k citations · 1.9k across the 35 of their papers we have counts for
7 papers · 1 filter
Balanced Linear Contextual Bandits
Maria Dimakopoulou, Zhengyuan Zhou, Susan Athey +1
Contextual bandit algorithms are sensitive to the estimation method of the outcome model as well as the exploration method used, particularly in the presence of rich heterogeneity…
Synthetic Difference in Differences
Dmitry Arkhangelsky, Susan Athey, David A. Hirshberg +2
We present a new estimator for causal effects with panel data that builds on insights behind the widely used difference in differences and synthetic control methods. Relative to th…
Offline Multi-Action Policy Learning: Generalization and Optimization
Zhengyuan Zhou, Susan Athey, Stefan Wager
In many settings, a decision-maker wishes to learn a rule, or policy, that maps from observable characteristics of an individual to an action. Examples include selecting offers, pr…
Design-based Analysis in Difference-In-Differences Settings with Staggered Adoption
Susan Athey, Guido Imbens
In this paper we study estimation of and inference for average treatment effects in a setting with panel data. We focus on the setting where units, e.g., individuals, firms, or sta…
Stable Prediction across Unknown Environments
Kun Kuang, Ruoxuan Xiong, Peng Cui +2
In many important machine learning applications, the training distribution used to learn a probabilistic classifier differs from the testing distribution on which the classifier wi…
Local Linear Forests
Rina Friedberg, Julie Tibshirani, Susan Athey +1
Random forests are a powerful method for non-parametric regression, but are limited in their ability to fit smooth signals, and can show poor predictive performance in the presence…