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.

翻译：算法预测作为一种高效分配社会资源的有前景解决方案正在兴起。推动其应用的一个基本假设是：此类系统对于识别需要干预的个体是必要的。我们提出了一个评估该假设的原则性框架：通过一个简单的数学模型，我们评估了在个体属于医院、社区或学校等更大单元的场景下，基于预测的分配方法的有效性。我们发现，仅当单元间不平等程度较低且干预预算较高时，基于预测的分配方法才优于仅使用单元层面汇总统计数据的基线方法。我们的结论在预测成本、处理效应异质性以及单元层面统计数据的可学习性等多种设定下均成立。综合而言，我们强调了通过预测提升干预效能的潜在局限性。