  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.
