Allocation Requires Prediction Only if Inequality Is Low

2024

Conference Paper

sf

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

@conference{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 (ICML)},
  pages = {45114--45153},
  series = {Proceedings of Machine Learning Research},
  editors = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix},
  publisher = {PMLR},
  month = jul,
  year = {2024},
  note = {equal contribution},
  doi = {},
  url = {https://proceedings.mlr.press/v235/shirali24a.html},
  month_numeric = {7}
}