Fair and Robust Estimation of Heterogeneous Treatment Effects for Policy Learning
Methodology
2023-12-21 v2 Machine Learning
Machine Learning
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.
Keywords
Cite
@article{arxiv.2306.03625,
title = {Fair and Robust Estimation of Heterogeneous Treatment Effects for Policy Learning},
author = {Kwangho Kim and José R. Zubizarreta},
journal= {arXiv preprint arXiv:2306.03625},
year = {2023}
}