Allocation Requires Prediction Only if Inequality Is Low

Ali Shirali, Rediet Abebe, Moritz Hardt
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:45114-45153, 2024.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v235-shirali24a, title = {Allocation Requires Prediction Only if Inequality Is Low}, author = {Shirali, Ali and Abebe, Rediet and Hardt, Moritz}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, pages = {45114--45153}, year = {2024}, editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix}, volume = {235}, series = {Proceedings of Machine Learning Research}, month = {21--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v235/main/assets/shirali24a/shirali24a.pdf}, url = {https://proceedings.mlr.press/v235/shirali24a.html}, 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.} }
Endnote
%0 Conference Paper %T Allocation Requires Prediction Only if Inequality Is Low %A Ali Shirali %A Rediet Abebe %A Moritz Hardt %B Proceedings of the 41st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Ruslan Salakhutdinov %E Zico Kolter %E Katherine Heller %E Adrian Weller %E Nuria Oliver %E Jonathan Scarlett %E Felix Berkenkamp %F pmlr-v235-shirali24a %I PMLR %P 45114--45153 %U https://proceedings.mlr.press/v235/shirali24a.html %V 235 %X 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.
APA
Shirali, A., Abebe, R. & Hardt, M.. (2024). Allocation Requires Prediction Only if Inequality Is Low. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:45114-45153 Available from https://proceedings.mlr.press/v235/shirali24a.html.

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