Problem definition: Mining for heterogeneous responses to an intervention is a crucial step for data-driven operations, for instance to personalize treatment or pricing. We investigate how to estimate price sensitivity from transaction-level data. In causal inference terms, we estimate heterogeneous treatment effects when (a) the response to treatment (here, whether a customer buys a product) is binary, and (b) treatment assignments are partially observed (here, full information is only available for purchased items). Methodology/Results: We propose a recursive partitioning procedure to estimate heterogeneous odds ratio, a widely used measure of treatment effect in medicine and social sciences. We integrate an adversarial imputation step to allow for robust estimation even in presence of partially observed treatment assignments. We validate our methodology on synthetic data and apply it to three case studies from political science, medicine, and revenue management. Managerial Implications: Our robust heterogeneous odds ratio estimation method is a simple and intuitive tool to quantify heterogeneity in patients or customers and personalize interventions, while lifting a central limitation in many revenue management data.

翻译：问题定义:为对干预措施作出不同反应而采矿是数据驱动作业的关键步骤,例如使治疗或定价个人化。我们调查如何从交易水平数据中估算价格敏感性。在因果推论中,我们估计治疗效果各异,(a) 治疗反应(这里的客户是否购买产品)是二进制的,和(b) 部分遵守治疗任务(这里只有购买的物品才能获得全部信息)。方法/结果:我们提出一种循环分割程序,以估计差异比率,这是在医学和社会科学中广泛使用的治疗效果衡量标准。我们采用了对抗性估算步骤,以允许即使在部分观察的治疗任务中也进行稳健的估计。我们验证了我们关于合成数据的方法,并将其应用于政治科学、医学和收入管理的三个案例研究。 管理影响:我们强大的差异比率估计方法是一个简单和直觉的工具,用以量化病人或客户的异质性,以及个人干预,同时解除许多收入管理数据的核心限制。