arXiv · 2306.03625
Fair and Robust Estimation of Heterogeneous Treatment Effects for Policy 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.
Explore related subjects
Keep this discovery
Explore connections, maps & timelines
Kwangho Kim, José R. Zubizarreta. 2023-12-20. Fair and Robust Estimation of Heterogeneous Treatment Effects for Policy Learning. https://arxiv.org/abs/2306.03625
Cite the original work for its findings. Save a collection to share your selection of sources.