Uplift modeling and Heterogeneous Treatment Effect (HTE) estimation aim at predicting the causal effect of an action, such as a medical treatment or a marketing campaign on a specific individual. In this paper, we focus on data from Randomized Controlled Experiments which guarantee causal interpretation of the outcomes. Class and treatment imbalance are important problems in uplift modeling/HTE, but classical undersampling or oversampling based approaches are hard to apply in this case since they distort the predicted effect. Calibration methods have been proposed in the past, however, they do not guarantee correct predictions. In this work, we propose an approach alternative to undersampling, based on flipping the class value of selected records. We show that the proposed approach does not distort the predicted effect and does not require calibration. The method is especially useful for models based on class variable transformation (modified outcome models). We address those models separately, designing a transformation scheme which guarantees correct predictions and addresses also the problem of treatment imbalance which is especially important for those models. Experiments fully confirm our theoretical results. Additionally, we demonstrate that our method is a viable alternative also for standard classification problems.

翻译：提升建模与异质处理效应估计旨在预测某项行动（如医疗干预或营销活动）对特定个体的因果效应。本文聚焦于来自随机对照实验的数据，这类数据保证了结果的可因果解释性。类别与处理不平衡是提升建模/异质处理效应估计中的重要问题，但传统的欠采样或过采样方法在此场景中难以应用，因为它们会扭曲预测效应。过去已有校准方法被提出，然而这些方法无法保证预测的正确性。在本研究中，我们提出一种替代欠采样的方法，其基于对选定记录的类别值进行翻转。我们证明所提方法不会扭曲预测效应，且无需校准。该方法尤其适用于基于类别变量转换的模型（修正结果模型）。我们针对此类模型单独处理，设计了一种转换方案，该方案能保证预测正确性，并同时解决了处理不平衡问题——这对该类模型尤为重要。实验充分验证了我们的理论结果。此外，我们证明该方法对于标准分类问题也是一种可行的替代方案。