Policy Learning with Asymmetric Counterfactual Utilities-Reference-Cited by-同舟云学术

Policy Learning with Asymmetric Counterfactual Utilities

Published:2024-02-13 Issue: Volume: Page:1-14
ISSN:0162-1459
Container-title:Journal of the American Statistical Association
language:en
Short-container-title:Journal of the American Statistical Association

Author:

Ben-Michael Eli¹^ORCID,Imai Kosuke²^ORCID,Jiang Zhichao³^ORCID

Affiliation:

1. Department of Statistics & Data Science and Heinz College of Information Systems & Public Policy, Carnegie Mellon University, Pittsburgh, PA

2. Department of Government and Department of Statistics, Institute for Quantitative Social Science, Harvard University, Cambridge, MA

3. School of Mathematics, Sun Yat-sen University, Guangzhou, Guangdong, China

Funder

Cisco Systems, Inc.

National Science Foundation

Sloan Foundation

National Natural Science of China

Fundamental Research Funds for the Central Universities, Sun Yat-sen University

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/01621459.2023.2300507

Reference48 articles.

1. Policy Learning With Observational Data

2. Fast learning rates for plug-in classifiers

3. Babii A. Chen X. Ghysels E. and Kumar R. (2021) “Binary Choice with Asymmetric Loss in a Data-Rich Environment: Theory and an Application to Racial Justice ” arxiv preprint arxiv:2010.08463.

4. Ben-Michael E. Greiner D. J. Imai K. and Jiang Z. (2021) “Safe Policy Learning through Extrapolation: Application to Pre-trial Risk Assessment ” arxiv preprint arxiv:2109.11679.

5. Theory and Applications of Robust Optimization

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