Qini curves have emerged as an attractive and popular approach for evaluating the benefit of data-driven targeting rules for treatment allocation. We propose a generalization of the Qini curve to multiple costly treatment arms, that quantifies the value of optimally selecting among both units and treatment arms at different budget levels. We develop an efficient algorithm for computing these curves and propose bootstrap-based confidence intervals that are exact in large samples for any point on the curve. These confidence intervals can be used to conduct hypothesis tests comparing the value of treatment targeting using an optimal combination of arms with using just a subset of arms, or with a non-targeting assignment rule ignoring covariates, at different budget levels. We demonstrate the statistical performance in a simulation experiment and an application to treatment targeting for election turnout.

翻译：Qini曲线已成为评估基于数据驱动的处理分配目标规则收益的一种受欢迎且富有吸引力的方法。我们提出将Qini曲线推广至多个有成本的处理臂，从而量化在不同预算水平下最优选择单元及处理臂的价值。我们开发了一种高效算法用于计算这些曲线，并提出了基于自助法的置信区间，该区间在大样本下对曲线上任意点均精确。这些置信区间可用于进行假设检验：在不同预算水平下，比较使用最优组合臂与仅使用子集臂或忽略协变量的非目标分配规则在处理目标上的价值。我们通过模拟实验以及针对选举投票率处理目标的应用，展示了统计性能。