1.6k citations · 1.9k across the 35 of their papers we have counts for
6 papers · 1 filter
The Heterogeneous Earnings Impact of Job Loss Across Workers, Establishments, and Markets
Susan Athey, Lisa K. Simon, Oskar N. Skans +2
Using rich Swedish administrative data, we apply causal machine learning methods to study how earnings losses after job displacement vary with observable characteristics that may b…
Proportional Response: Contextual Bandits for Simple and Cumulative Regret Minimization
Sanath Kumar Krishnamurthy, Ruohan Zhan, Susan Athey +1
In many applications, e.g. in healthcare and e-commerce, the goal of a contextual bandit may be to learn an optimal treatment assignment policy at the end of the experiment. That i…
Qini Curves for Multi-Armed Treatment Rules
Erik Sverdrup, Han Wu, Susan Athey +1
Qini curves have emerged as an attractive and popular approach for evaluating the benefit of data-driven targeting rules for treatment allocation. We propose a generalization of th…
Federated Offline Policy Learning
Aldo Gael Carranza, Susan Athey
We consider the problem of learning personalized decision policies from observational bandit feedback data across multiple heterogeneous data sources. In our approach, we introduce…
Torch-Choice: A PyTorch Package for Large-Scale Choice Modeling with Python
Tianyu Du, Ayush Kanodia, Susan Athey
The is an open-source library for flexible, fast choice modeling with Python and PyTorch. provides a data s…
Synthetic Difference In Differences Estimation
Damian Clarke, Daniel Pailañir, Susan Athey +1
In this paper, we describe a computational implementation of the Synthetic difference-in-differences (SDID) estimator of Arkhangelsky et al. (2021) for Stata. Synthetic difference-…