[2407.01111] Proximity Matters: Local Proximity Enhanced Balancing for Treatment Effect Estimation

Computer Science > Machine Learning arXiv:2407.01111 (cs) [Submitted on 1 Jul 2024 (v1), last revised 25 Mar 2026 (this version, v2)] Title:Proximity Matters: Local Proximity Enhanced Balancing for Treatment Effect Estimation Authors:Hao Wang, Zhichao Chen, Zhaoran Liu, Xu Chen, Haoxuan Li, Zhouchen Lin View a PDF of the paper titled Proximity Matters: Local Proximity Enhanced Balancing for Treatment Effect Estimation, by Hao Wang and 5 other authors View PDF HTML (experimental) Abstract: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-enhanced CounterFactual Regression (CFR-Pro) to exploit proximity for enhancing representation balancing within the HTE estimation context. Specifically, we introduce a pair-wise proximity regularizer based on optimal transport to incorporate the local proximity in discrepancy calculation. However, the curse of dimensionality renders the proximity measure and discrepancy estimation ineffective -- exacerbated by limited data availability for HTE estimation. To handle this problem, we further develop an informative subspace projector, which trades off minimal distan...