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定位规则收益的一种富有吸引力且流行的方法。我们将Qini曲线推广至多成本治疗臂的情形，该推广量化了在不同预算水平下最优选择试验单元及治疗臂的价值。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具有精确性。这些置信区间可用于进行假设检验：在相同预算水平下，比较使用最优治疗臂组合与仅使用子集治疗臂、或忽略协变量的非定位分配规则的治疗定位价值。我们通过模拟实验以及选举参与度治疗定位的应用实例展示了其统计性能。