Allocation Requires Prediction Only if Inequality Is Low
ArXiv
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
| Author(s): | Shirali, Ali and Abebe, Rediet* and Hardt, Moritz* |
| Links: | |
| Book Title: | Proceedings of the 41st International Conference on Machine Learning (ICML 2024) |
| Year: | 2024 |
| Month: | July |
| Publisher: | PMLR |
| Project(s): | |
| BibTeX Type: | Conference Paper (inproceedings) |
| Event Name: | The Forty-First International Conference on Machine Learning (ICML) |
| State: | Published |
| URL: | https://proceedings.mlr.press/v235/shirali24a.html |
| Electronic Archiving: | grant_archive |
| Note: | *equal contribution |
BibTeX
@inproceedings{shirali2024allocation,
title = {Allocation Requires Prediction Only if Inequality Is Low},
booktitle = {Proceedings of the 41st International Conference on Machine Learning (ICML 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},
publisher = {PMLR},
month = jul,
year = {2024},
note = {*equal contribution},
author = {Shirali, Ali and Abebe, Rediet* and Hardt, Moritz*},
url = {https://proceedings.mlr.press/v235/shirali24a.html},
month_numeric = {7}
}