云讲堂预告 | 吴韩：多臂治疗规则的 Qini 曲线

Qini曲线已经成为评估基于数据驱动的治疗分配规则效益的一种有吸引力和流行的方法。我们提出了一种将Qini曲线推广到多个昂贵治疗分支的方法，该方法在不同预算水平上定量衡量在单位和治疗分支之间进行最优选择的价值。我们开发了一种高效的算法来计算这些曲线，并提出了基于自助法的置信区间，对于曲线上的任何点，在大样本下是精确的。这些置信区间可用于在不同预算水平上进行假设检验，比较使用最佳分支组合的治疗定位价值与仅使用部分分支或忽略协变量的非定位分配规则的价值。我们通过模拟实验和针对选举投票的治疗定位应用来展示其统计性能。

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.