Proximity Matters: Local Proximity Enhanced Balancing for Treatment Effect Estimation

TL;DR

This paper introduces CFR-Pro, a novel method that leverages local proximity and optimal transport to improve treatment effect estimation from observational data, effectively reducing bias and enhancing matching accuracy.

Contribution

It proposes a proximity-enhanced representation balancing approach using a subspace projector and optimal transport, addressing the limitations of global alignment methods.

Findings

CFR-Pro accurately matches units across treatment groups.

It significantly reduces treatment selection bias.

The method outperforms existing approaches in experiments.

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 distance precision for improved sample complexity. Extensive experiments demonstrate that CFR-Pro accurately matches units across different treatment groups, effectively mitigates treatment selection bias, and significantly outperforms competitors. Code is available at https://github.com/HowardZJU/CFR-Pro.