Allocation Requires Prediction Only if Inequality Is Low
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866911927061446656 |
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| author | Shirali, Ali Abebe, Rediet Hardt, Moritz |
| author_facet | Shirali, Ali Abebe, Rediet Hardt, Moritz |
| contents | Algorithmic predictions are emerging as a promising solution concept for efficiently allocating societal resources. Fueling their use is an underlying assumption that such systems are necessary to identify individuals for interventions. We propose a principled framework for assessing this assumption: Using a simple mathematical model, we evaluate the efficacy of prediction-based allocations in settings where individuals belong to larger units such as hospitals, neighborhoods, or schools. We find that prediction-based allocations outperform baseline methods using aggregate unit-level statistics only when between-unit inequality is low and the intervention budget is high. Our results hold for a wide range of settings for the price of prediction, treatment effect heterogeneity, and unit-level statistics' learnability. Combined, we highlight the potential limits to improving the efficacy of interventions through prediction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_13882 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Allocation Requires Prediction Only if Inequality Is Low Shirali, Ali Abebe, Rediet Hardt, Moritz Machine Learning Computers and Society Theoretical Economics Algorithmic predictions are emerging as a promising solution concept for efficiently allocating societal resources. Fueling their use is an underlying assumption that such systems are necessary to identify individuals for interventions. We propose a principled framework for assessing this assumption: Using a simple mathematical model, we evaluate the efficacy of prediction-based allocations in settings where individuals belong to larger units such as hospitals, neighborhoods, or schools. We find that prediction-based allocations outperform baseline methods using aggregate unit-level statistics only when between-unit inequality is low and the intervention budget is high. Our results hold for a wide range of settings for the price of prediction, treatment effect heterogeneity, and unit-level statistics' learnability. Combined, we highlight the potential limits to improving the efficacy of interventions through prediction. |
| title | Allocation Requires Prediction Only if Inequality Is Low |
| topic | Machine Learning Computers and Society Theoretical Economics |
| url | https://arxiv.org/abs/2406.13882 |