Uplift modeling, also known as individual treatment effect (ITE) estimation, is an important approach for data-driven decision making that aims to identify the causal impact of an intervention on individuals. This paper introduces a new benchmark dataset for uplift modeling focused on churn prediction, coming from a telecom company in Belgium, Orange Belgium. Churn, in this context, refers to customers terminating their subscription to the telecom service. This is the first publicly available dataset offering the possibility to evaluate the efficiency of uplift modeling on the churn prediction problem. Moreover, its unique characteristics make it more challenging than the few other public uplift datasets.

翻译：提升建模（亦称个体处理效应估计）是数据驱动决策的重要方法，旨在识别干预措施对个体产生的因果影响。本文介绍了一个聚焦流失预测的电信行业提升建模新基准数据集，该数据集源自比利时电信公司Orange Belgium。此处"流失"指客户终止电信服务订阅的行为。这是首个公开可用的、支持评估提升建模在流失预测问题上效能的数据集。此外，其独特特征使其比现有少数公开提升数据集更具挑战性。