Uplift modeling is widely used in performance marketing to estimate effects of promotion campaigns (e.g., increase of customer retention rate). Since it is impossible to observe outcomes of a recipient in treatment (e.g., receiving a certain promotion) and control (e.g., without promotion) groups simultaneously (i.e., counter-factual), uplift models are mainly trained on instances of treatment and control groups separately to form two models respectively, and uplifts are predicted by the difference of predictions from these two models (i.e., two-model method). When responses are noisy and the treatment effect is fractional, induced individual uplift predictions will be inaccurate, resulting in targeting undesirable customers. Though it is impossible to obtain the ideal ground-truth individual uplifts, known as Individual Treatment Effects (ITEs), alternatively, an average uplift of a group of users, called Average Treatment Effect (ATE), can be observed from experimental deliveries. Upon this, similar to Multiple Instance Learning (MIL) in which each training sample is a bag of instances, our framework sums up individual user uplift predictions for each bag of users as its bag-wise ATE prediction, and regularizes it to its ATE label, thus learning more accurate individual uplifts. Additionally, to amplify the fractional treatment effect, bags are composed of instances with adjacent individual uplift predictions, instead of random instances. Experiments conducted on two datasets show the effectiveness and universality of the proposed framework.

翻译：提升建模广泛应用于效果营销中，用于评估促销活动（例如客户留存率的提升）的效果。由于无法同时观察同一对象在实验组（如接受特定促销）和对照组（如未接受促销）中的结果（即反事实情况），提升模型主要分别使用实验组和对照组的实例进行训练，形成两个独立模型，并通过这两个模型预测结果的差值来预测提升效果（即两模型法）。当响应存在噪声且处理效应较小（分数级）时，个体提升预测的结果将不准确，从而导致对不理想客户的错误定位。尽管无法获取理想的个体真实提升——即个体处理效应（ITE），但可以通过实验投放观测到用户群体的平均提升——即平均处理效应（ATE）。基于此，类似于多实例学习（MIL）中每个训练样本为一个实例包的方法，我们的框架将每个用户包中个体提升预测结果求和作为该包的包级ATE预测，并通过正则化使其拟合ATE标签，从而学习更准确的个体提升。此外，为放大分数级处理效应，包由具有相邻个体提升预测结果的实例组成，而非随机选取的实例。在两个数据集上的实验表明，所提框架具有有效性和普适性。