In this paper, we propose a novel matching strategy to correct for confounding in uplift modeling. Our method, called propensity score oversampling and matching (ProSOM), extends the well-known propensity score matching (PSM) technique by addressing one of its main limitations: dealing with small datasets that face an imbalance in the distribution of the causal variable. Apart from this, we also face the additional complexity of dealing with class labels. The proposed method establishes a parallel between uplift modeling and class-imbalance classification as it extends existing oversampling techniques to create synthetic elements from the treatment group. We design an algorithm that performs classaware data oversampling in the treatment group, and then it matches samples from this group with the control group. This can be seen as a novel hybrid undersampling-oversampling solution for causal learning. Experiments on five datasets show the virtues of ProSOM in terms of predictive performance, achieving the best Qini coefficient for all five datasets in relation to PSM and other resampling solutions.

中文翻译：

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用于提升建模的倾向得分过采样和匹配

在本文中，我们提出了一种新颖的匹配策略来纠正隆起建模中的混杂因素。我们的方法称为倾向评分过采样和匹配 (ProSOM)，通过解决其主要局限性之一来扩展众所周知的倾向评分匹配 (PSM) 技术：处理因果变量分布不平衡的小数据集。除此之外，我们还面临着处理类标签的额外复杂性。所提出的方法在提升建模和类不平衡分类之间建立了并行性，因为它扩展了现有的过采样技术以从治疗组创建合成元素。我们设计了一种算法，在治疗组中执行类感知数据过采样，然后将该组的样本与对照组进行匹配。这可以被视为一种新颖的因果学习混合欠采样-过采样解决方案。对五个数据集的实验显示了 ProSOM 在预测性能方面的优点，相对于 PSM 和其他重采样解决方案，所有五个数据集都实现了最佳 Qini 系数。