Proximity Matters: Local Proximity Enhanced Balancing for Treatment Effect Estimation
Summary: arXiv:2407.01111v2 Announce Type: replace-cross
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.
Introduction
Heterogeneous treatment effect estimation is crucial for accurately assessing the impact of treatment interventions in various fields, including healthcare and social sciences. Traditional methods often struggle with treatment selection bias, leading to inaccurate estimates of treatment effects. This article introduces a novel approach, CFR-Pro, which emphasizes local proximity to improve treatment effect estimation.
Challenges in HTE Estimation
The primary challenge in HTE estimation lies in the treatment selection bias, which can skew results and lead to misguided conclusions. Current methods typically focus on minimizing global discrepancies between treatment groups, which may not adequately address the nuances of local relationships among similar units.
Proposed Method: CFR-Pro
CFR-Pro introduces a pair-wise proximity regularizer based on optimal transport, allowing researchers to incorporate local proximity into the discrepancy calculation. This method aims to enhance representation balancing by recognizing that similar units tend to yield similar outcomes.
- Local Proximity: By focusing on local similarities, CFR-Pro can better account for nuanced variations in treatment effects.
- Optimal Transport: This mathematical approach allows for the effective mapping of distributions, facilitating improved estimation accuracy.
- Subspace Projector: A key innovation, this projector helps manage the curse of dimensionality, improving sample complexity without sacrificing precision.
Experimental Results
Extensive experiments were conducted to evaluate the performance of CFR-Pro. The results indicate that the method effectively matches units across different treatment groups, thereby mitigating treatment selection bias. Notably, CFR-Pro significantly outperformed existing competitors in terms of accuracy and reliability in HTE estimation.
Conclusion
In summary, the proposed CFR-Pro method offers a promising advancement in HTE estimation by leveraging local proximity to enhance representation balancing. The integration of a pair-wise proximity regularizer and an informative subspace projector addresses critical challenges in treatment selection bias and dimensionality issues, paving the way for more accurate treatment effect estimations.
Further Information
For researchers interested in implementing CFR-Pro, the code is available at GitHub – CFR-Pro.