Allocation Requires Prediction Only if Inequality Is Low
This work addresses resource allocation challenges in domains like healthcare and education, highlighting potential limits to prediction-based interventions, and is incremental in refining assumptions about their necessity.
The paper tackles the problem of efficiently allocating societal resources using algorithmic predictions, finding that prediction-based allocations outperform baseline methods only when between-unit inequality is low and the intervention budget is high.
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.