Learning Treatment Allocations with Risk Control Under Partial Identifiability

Sofia Ek, Dave Zachariah
Proceedings of the 43rd International Conference on Machine Learning, PMLR 306:27794-27808, 2026.

Abstract

Learning beneficial treatment allocations for a patient population is an important problem in precision medicine. For such allocations, a certain proportion of treated patients may not receive any benefit. This proportion of unnecessary treated represents a ‘treatment risk’ which is a waste of resources and may, in addition, expose patients to unnecessary adverse effects. Therefore, we aim to control the treatment risk when learning beneficial allocations. This learning problem is complicated by the fact that the treatment risk is generally not identifiable from either randomized trial or observational data. We propose a certifiable learning method that controls treatment risk, using finite samples in the partially identified setting. The method is illustrated using both simulated and real data.

Cite this Paper

BibTeX
@InProceedings{pmlr-v306-ek26a, title = {Learning Treatment Allocations with Risk Control Under Partial Identifiability}, author = {Ek, Sofia and Zachariah, Dave}, booktitle = {Proceedings of the 43rd International Conference on Machine Learning}, pages = {27794--27808}, year = {2026}, editor = {Zhang, Tong and Dudik, Miroslav and Jaggi, Martin and Agarwal, Alekh and Li, Sharon and Schuurmans, Dale and Zhu, Jerry and Berkenkamp, Felix and Dong, Hanze and Bietti, Alberto}, volume = {306}, series = {Proceedings of Machine Learning Research}, month = {06--11 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v306/main/assets/ek26a/ek26a.pdf}, url = {https://proceedings.mlr.press/v306/ek26a.html}, abstract = {Learning beneficial treatment allocations for a patient population is an important problem in precision medicine. For such allocations, a certain proportion of treated patients may not receive any benefit. This proportion of unnecessary treated represents a ‘treatment risk’ which is a waste of resources and may, in addition, expose patients to unnecessary adverse effects. Therefore, we aim to control the treatment risk when learning beneficial allocations. This learning problem is complicated by the fact that the treatment risk is generally not identifiable from either randomized trial or observational data. We propose a certifiable learning method that controls treatment risk, using finite samples in the partially identified setting. The method is illustrated using both simulated and real data.} }
Endnote
%0 Conference Paper %T Learning Treatment Allocations with Risk Control Under Partial Identifiability %A Sofia Ek %A Dave Zachariah %B Proceedings of the 43rd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2026 %E Tong Zhang %E Miroslav Dudik %E Martin Jaggi %E Alekh Agarwal %E Sharon Li %E Dale Schuurmans %E Jerry Zhu %E Felix Berkenkamp %E Hanze Dong %E Alberto Bietti %F pmlr-v306-ek26a %I PMLR %P 27794--27808 %U https://proceedings.mlr.press/v306/ek26a.html %V 306 %X Learning beneficial treatment allocations for a patient population is an important problem in precision medicine. For such allocations, a certain proportion of treated patients may not receive any benefit. This proportion of unnecessary treated represents a ‘treatment risk’ which is a waste of resources and may, in addition, expose patients to unnecessary adverse effects. Therefore, we aim to control the treatment risk when learning beneficial allocations. This learning problem is complicated by the fact that the treatment risk is generally not identifiable from either randomized trial or observational data. We propose a certifiable learning method that controls treatment risk, using finite samples in the partially identified setting. The method is illustrated using both simulated and real data.
APA
Ek, S. & Zachariah, D.. (2026). Learning Treatment Allocations with Risk Control Under Partial Identifiability. Proceedings of the 43rd International Conference on Machine Learning, in Proceedings of Machine Learning Research 306:27794-27808 Available from https://proceedings.mlr.press/v306/ek26a.html.

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