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曲线已成为评估数据驱动治疗分配目标规则效益的流行且具吸引力的方法。我们提出将Qini曲线推广至多个高成本治疗臂，该推广量化了在不同预算水平下最优选择对象和治疗臂的价值。我们开发了一种高效算法来计算这些曲线，并提出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具有精确性。这些置信区间可用于假设检验，比较在利用最优治疗臂组合与仅使用子集臂，或不考虑协变量的非目标分配规则在不同预算水平下的治疗目标价值。我们通过仿真实验和选举投票治疗目标应用案例展示了统计性能。