  Heterogeneous treatment effect (HTE) estimation from observational data poses
significant challenges due to treatment selection bias. Existing methods
address this bias by minimizing distribution discrepancies between treatment
groups in latent space, focusing on global alignment. However, the fruitful
aspect of local proximity, where similar units exhibit similar outcomes, is
often overlooked. In this study, we propose Proximity-aware Counterfactual
Regression (PCR) to exploit proximity for representation balancing within the
HTE estimation context. Specifically, we introduce a local proximity
preservation regularizer based on optimal transport to depict the local
proximity in discrepancy calculation. Furthermore, to overcome the curse of
dimensionality that renders the estimation of discrepancy ineffective,
exacerbated by limited data availability for HTE estimation, we develop an
informative subspace projector, which trades off minimal distance precision for
improved sample complexity. Extensive experiments demonstrate that PCR
accurately matches units across different treatment groups, effectively
mitigates treatment selection bias, and significantly outperforms competitors.
Code is available at https://anonymous.4open.science/status/ncr-B697.
