Statistical disparity between distinct treatment groups is one of the most
significant challenges for estimating Conditional Average Treatment Effects
(CATE). To address this, we introduce a model-agnostic data augmentation method
that imputes the counterfactual outcomes for a selected subset of individuals.
Specifically, we utilize contrastive learning to learn a representation space
and a similarity measure such that in the learned representation space close
individuals identified by the learned similarity measure have similar potential
outcomes. This property ensures reliable imputation of counterfactual outcomes
for the individuals with close neighbors from the alternative treatment group.
By augmenting the original dataset with these reliable imputations, we can
effectively reduce the discrepancy between different treatment groups, while
inducing minimal imputation error. The augmented dataset is subsequently
employed to train CATE estimation models. Theoretical analysis and experimental
studies on synthetic and semi-synthetic benchmarks demonstrate that our method
achieves significant improvements in both performance and robustness to
overfitting across state-of-the-art models.

引入了一种无模型数据增强方法来解决不同治疗组之间的统计偏差问题，通过学习表示空间和相似度度量，确保接近的个体拥有相似的潜在结果，从而可靠地预测替代治疗组中个体的反事实结局，并有效减小不同治疗组之间的差异，进而提高现有模型的性能和抗过拟合能力的实验研究表明方法具有显著的改进。