Fair and Robust Estimation of Heterogeneous Treatment Effects for Policy Learning

Kwangho Kim, Jose R Zubizarreta
Proceedings of the 40th International Conference on Machine Learning, PMLR 202:16997-17014, 2023.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v202-kim23ab, title = {Fair and Robust Estimation of Heterogeneous Treatment Effects for Policy Learning}, author = {Kim, Kwangho and Zubizarreta, Jose R}, booktitle = {Proceedings of the 40th International Conference on Machine Learning}, pages = {16997--17014}, year = {2023}, editor = {Krause, Andreas and Brunskill, Emma and Cho, Kyunghyun and Engelhardt, Barbara and Sabato, Sivan and Scarlett, Jonathan}, volume = {202}, series = {Proceedings of Machine Learning Research}, month = {23--29 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v202/kim23ab/kim23ab.pdf}, url = {https://proceedings.mlr.press/v202/kim23ab.html}, 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.} }
Endnote
%0 Conference Paper %T Fair and Robust Estimation of Heterogeneous Treatment Effects for Policy Learning %A Kwangho Kim %A Jose R Zubizarreta %B Proceedings of the 40th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2023 %E Andreas Krause %E Emma Brunskill %E Kyunghyun Cho %E Barbara Engelhardt %E Sivan Sabato %E Jonathan Scarlett %F pmlr-v202-kim23ab %I PMLR %P 16997--17014 %U https://proceedings.mlr.press/v202/kim23ab.html %V 202 %X 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.
APA
Kim, K. & Zubizarreta, J.R.. (2023). Fair and Robust Estimation of Heterogeneous Treatment Effects for Policy Learning. Proceedings of the 40th International Conference on Machine Learning, in Proceedings of Machine Learning Research 202:16997-17014 Available from https://proceedings.mlr.press/v202/kim23ab.html.

Related Material