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Allocation Requires Prediction Only if Inequality Is Low

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  • Ali Shirali
  • Rediet Abebe
  • Moritz Hardt

Abstract

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.

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  • Ali Shirali & Rediet Abebe & Moritz Hardt, 2024. "Allocation Requires Prediction Only if Inequality Is Low," Papers 2406.13882, arXiv.org.
    • Handle: RePEc:arx:papers:2406.13882
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    References listed on IDEAS

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    7. Susan Athey & Stefan Wager, 2021. "Policy Learning With Observational Data," Econometrica, Econometric Society, vol. 89(1), pages 133-161, January.
    8. Juan C. Perdomo & Tolani Britton & Moritz Hardt & Rediet Abebe, 2023. "Difficult Lessons on Social Prediction from Wisconsin Public Schools," Papers 2304.06205, arXiv.org, revised Sep 2023.
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