Evaluating the Effectiveness of Index-Based Treatment Allocation
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909111442997248 |
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| author | Boehmer, Niclas Nair, Yash Shah, Sanket Janson, Lucas Taneja, Aparna Tambe, Milind |
| author_facet | Boehmer, Niclas Nair, Yash Shah, Sanket Janson, Lucas Taneja, Aparna Tambe, Milind |
| contents | When resources are scarce, an allocation policy is needed to decide who receives a resource. This problem occurs, for instance, when allocating scarce medical resources and is often solved using modern ML methods. This paper introduces methods to evaluate index-based allocation policies -- that allocate a fixed number of resources to those who need them the most -- by using data from a randomized control trial. Such policies create dependencies between agents, which render the assumptions behind standard statistical tests invalid and limit the effectiveness of estimators. Addressing these challenges, we translate and extend recent ideas from the statistics literature to present an efficient estimator and methods for computing asymptotically correct confidence intervals. This enables us to effectively draw valid statistical conclusions, a critical gap in previous work. Our extensive experiments validate our methodology in practical settings, while also showcasing its statistical power. We conclude by proposing and empirically verifying extensions of our methodology that enable us to reevaluate a past randomized control trial to evaluate different ML allocation policies in the context of a mHealth program, drawing previously invisible conclusions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_11771 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Evaluating the Effectiveness of Index-Based Treatment Allocation Boehmer, Niclas Nair, Yash Shah, Sanket Janson, Lucas Taneja, Aparna Tambe, Milind Machine Learning Artificial Intelligence Methodology When resources are scarce, an allocation policy is needed to decide who receives a resource. This problem occurs, for instance, when allocating scarce medical resources and is often solved using modern ML methods. This paper introduces methods to evaluate index-based allocation policies -- that allocate a fixed number of resources to those who need them the most -- by using data from a randomized control trial. Such policies create dependencies between agents, which render the assumptions behind standard statistical tests invalid and limit the effectiveness of estimators. Addressing these challenges, we translate and extend recent ideas from the statistics literature to present an efficient estimator and methods for computing asymptotically correct confidence intervals. This enables us to effectively draw valid statistical conclusions, a critical gap in previous work. Our extensive experiments validate our methodology in practical settings, while also showcasing its statistical power. We conclude by proposing and empirically verifying extensions of our methodology that enable us to reevaluate a past randomized control trial to evaluate different ML allocation policies in the context of a mHealth program, drawing previously invisible conclusions. |
| title | Evaluating the Effectiveness of Index-Based Treatment Allocation |
| topic | Machine Learning Artificial Intelligence Methodology |
| url | https://arxiv.org/abs/2402.11771 |