基于RCT数据的隆升模型评价方法改进
Improving uplift model evaluation on RCT data
摘要：
估计处理效应是数据分析师最具挑战性和最重要的任务之一。在许多应用中，如在线营销和个性化医疗，需要将治疗分配给产生高积极处理效应的个体。可持续发展模型有助于选择合适的个体进行治疗，并最大限度地提高整体处理效应（提升）。隆升建模的一个主要挑战是模型评价。以前的文献建议的方法，如Qini曲线和转换后的结果均方误差。然而，这些指标受到方差的影响：它们的评估受到数据中随机噪声的强烈影响，这使得它们的信号在一定程度上是任意的。我们从理论上分析了隆起评估指标的方差，并推导出可能的方差减少的方法，这是基于统计调整的结果。我们推导出简单的条件下，方差减小方法提高隆起评估指标，并实证证明其模拟和真实世界的数据的好处。我们的论文提供了强有力的证据，支持在RCT数据上评估隆升模型时默认应用建议的方差缩减程序。
英文摘要：
Estimating treatment effects is one of the most challenging and important tasks of data analysts. In many applications, like online marketing and personalized medicine, treatment needs to be allocated to the individuals where it yields a high positive treatment effect. Uplift models help select the right individuals for treatment and maximize the overall treatment effect (uplift). A major challenge in uplift modeling concerns model evaluation. Previous literature suggests methods like the Qini curve and the transformed outcome mean squared error. However, these metrics suffer from variance: their evaluations are strongly affected by random noise in the data, which renders their signals, to a certain degree, arbitrary. We theoretically analyze the variance of uplift evaluation metrics and derive possible methods of variance reduction, which are based on statistical adjustment of the outcome. We derive simple conditions under which the variance reduction methods improve the uplift evaluation metrics and empirically demonstrate their benefits on simulated and real-world data. Our paper provides strong evidence in favor of applying the suggested variance reduction procedures by default when evaluating uplift models on RCT data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
