Fair Policy Targeting-Reference-Cited by-同舟云学术

Fair Policy Targeting

Published:2023-01-09 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:

Viviano Davide¹,Bradic Jelena²

Affiliation:

1. Stanford Graduate School of Business, Stanford, CA, and Department of Economics, Harvard University, Cambridge, MA

2. Department of Mathematics and Halicioğlu Data Science Institute, University of California at San Diego, La Jolla, CA

Funder

NSF

Publisher

Informa UK Limited

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/01621459.2022.2142591

Reference56 articles.

1. Armstrong, T., and Shen, S. (2015), “Inference on Optimal Treatment Assignments,” Available at SSRN 2592479.

2. Policy Learning With Observational Data

3. Balashankar, A., Lees, A., Welty, C., and Subramanian, L. (2019), “What is Fair? Exploring Pareto-Efficiency for Fairness Constrained Classifiers,” arXiv preprint arXiv:1910.14120.

4. Inferring welfare maximizing treatment assignment under budget constraints

5. Chernozhukov, V., Newey, W. K., and Robins, J. (2018), “Double/de-biased Machine Learning using Regularized Riesz Representers,” Technical report, cemmap working paper.

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