| Autor: |
Kim, Kwangho, Zubizarreta, José R. |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Proceedings of the 40 th International Conference on Machine Learning, Honolulu, Hawaii, USA. PMLR 202, 16997--17014, 2023 |
| Druh dokumentu: |
Working Paper |
| Popis: |
We propose a simple and general framework for nonparametric estimation of heterogeneous treatment effects under fairness constraints. Under standard regularity conditions, we show that the resulting estimators possess the double robustness property. We use this framework to characterize the trade-off between fairness and the maximum welfare achievable by the optimal policy. We evaluate the methods in a simulation study and illustrate them in a real-world case study. |
| Databáze: |
arXiv |
| Externí odkaz: |
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