Learning Treatment Allocations with Risk Control Under Partial Identifiability
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909608803565568 |
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| author | Ek, Sofia Zachariah, Dave |
| author_facet | Ek, Sofia Zachariah, Dave |
| contents | Learning beneficial treatment allocations for a patient population is an important problem in precision medicine. Many treatments come with adverse side effects that are not commensurable with their potential benefits. Patients who do not receive benefits after such treatments are thereby subjected to unnecessary harm. This is a `treatment risk' that we aim to control when learning beneficial allocations. The constrained learning problem is challenged by the fact that the treatment risk is not in general identifiable using either randomized trial or observational data. We propose a certifiable learning method that controls the treatment risk with finite samples in the partially identified setting. The method is illustrated using both simulated and real data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_08378 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning Treatment Allocations with Risk Control Under Partial Identifiability Ek, Sofia Zachariah, Dave Machine Learning Learning beneficial treatment allocations for a patient population is an important problem in precision medicine. Many treatments come with adverse side effects that are not commensurable with their potential benefits. Patients who do not receive benefits after such treatments are thereby subjected to unnecessary harm. This is a `treatment risk' that we aim to control when learning beneficial allocations. The constrained learning problem is challenged by the fact that the treatment risk is not in general identifiable using either randomized trial or observational data. We propose a certifiable learning method that controls the treatment risk with finite samples in the partially identified setting. The method is illustrated using both simulated and real data. |
| title | Learning Treatment Allocations with Risk Control Under Partial Identifiability |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2505.08378 |