Advancing Fairness in Precision Medicine: A Universal Framework for Optimal Treatment Estimation in Censored Data

Hongni Wang, Junxi Zhang, Na Li, Linglong Kong, Bei Jiang, Xiaodong Yan
Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, PMLR 258:4762-4770, 2025.

Abstract

In healthcare and precision medicine, estimating optimal treatment regimes for right-censored data while ensuring fairness across ethnic subgroups is crucial but remains underexplored. The problem presents two key challenges: measuring heterogeneous treatment effects (HTE) under fairness constraints and dealing with censoring mechanisms. We propose a general framework for estimating HTE using nonparametric methods and integrating user-controllable fairness constraints to address these problems. Under mild regularization assumptions, our method is theoretically grounded, demonstrating the double robustness property of the HTE estimator. Using this framework, we demonstrate that optimal treatment strategies balance fairness and utility. Using extensive simulations and real-world data analysis, we uncovered the potential of this method to guide the selection of treatment methods that are equitable and effective.

Cite this Paper

BibTeX
@InProceedings{pmlr-v258-wang25k, title = {Advancing Fairness in Precision Medicine: A Universal Framework for Optimal Treatment Estimation in Censored Data}, author = {Wang, Hongni and Zhang, Junxi and Li, Na and Kong, Linglong and Jiang, Bei and Yan, Xiaodong}, booktitle = {Proceedings of The 28th International Conference on Artificial Intelligence and Statistics}, pages = {4762--4770}, year = {2025}, editor = {Li, Yingzhen and Mandt, Stephan and Agrawal, Shipra and Khan, Emtiyaz}, volume = {258}, series = {Proceedings of Machine Learning Research}, month = {03--05 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v258/main/assets/wang25k/wang25k.pdf}, url = {https://proceedings.mlr.press/v258/wang25k.html}, abstract = {In healthcare and precision medicine, estimating optimal treatment regimes for right-censored data while ensuring fairness across ethnic subgroups is crucial but remains underexplored. The problem presents two key challenges: measuring heterogeneous treatment effects (HTE) under fairness constraints and dealing with censoring mechanisms. We propose a general framework for estimating HTE using nonparametric methods and integrating user-controllable fairness constraints to address these problems. Under mild regularization assumptions, our method is theoretically grounded, demonstrating the double robustness property of the HTE estimator. Using this framework, we demonstrate that optimal treatment strategies balance fairness and utility. Using extensive simulations and real-world data analysis, we uncovered the potential of this method to guide the selection of treatment methods that are equitable and effective.} }
Endnote
%0 Conference Paper %T Advancing Fairness in Precision Medicine: A Universal Framework for Optimal Treatment Estimation in Censored Data %A Hongni Wang %A Junxi Zhang %A Na Li %A Linglong Kong %A Bei Jiang %A Xiaodong Yan %B Proceedings of The 28th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2025 %E Yingzhen Li %E Stephan Mandt %E Shipra Agrawal %E Emtiyaz Khan %F pmlr-v258-wang25k %I PMLR %P 4762--4770 %U https://proceedings.mlr.press/v258/wang25k.html %V 258 %X In healthcare and precision medicine, estimating optimal treatment regimes for right-censored data while ensuring fairness across ethnic subgroups is crucial but remains underexplored. The problem presents two key challenges: measuring heterogeneous treatment effects (HTE) under fairness constraints and dealing with censoring mechanisms. We propose a general framework for estimating HTE using nonparametric methods and integrating user-controllable fairness constraints to address these problems. Under mild regularization assumptions, our method is theoretically grounded, demonstrating the double robustness property of the HTE estimator. Using this framework, we demonstrate that optimal treatment strategies balance fairness and utility. Using extensive simulations and real-world data analysis, we uncovered the potential of this method to guide the selection of treatment methods that are equitable and effective.
APA
Wang, H., Zhang, J., Li, N., Kong, L., Jiang, B. & Yan, X.. (2025). Advancing Fairness in Precision Medicine: A Universal Framework for Optimal Treatment Estimation in Censored Data. Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 258:4762-4770 Available from https://proceedings.mlr.press/v258/wang25k.html.

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