Allocation Requires Prediction Only if Inequality Is Low
<|reference_start|>Allocation Requires Prediction Only if Inequality Is Low: 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.<|reference_end|>
@article{shirali2024allocation,
    title={Allocation Requires Prediction Only if Inequality Is Low},
    author={Ali Shirali, Rediet Abebe, Moritz Hardt},
    journal={arXiv preprint arXiv:2406.13882},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.13882},
    primaryClass={cs.LG cs.CY econ.TH}
}