Fair and Robust Estimation of Heterogeneous Treatment Effects for Policy Learning

• URL: http://arxiv.org/abs/2306.03625v2
• Date: Wed, 20 Dec 2023 11:24:12 GMT
• Title: Fair and Robust Estimation of Heterogeneous Treatment Effects for Policy Learning
• Authors: Kwangho Kim and Jos\'e R. Zubizarreta
• Abstract summary: 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.
• Score: 2.356908851188234
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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