Model Averaging for Heterogeneous Treatment Effects via Proximity Matching

Accurate estimation of heterogeneous treatment effects (HTEs) serves as a cornerstone of personalized decision-making, especially in observational studies where treatment assignment is not randomized. However, the presence of confounding and complex covariate structures poses significant challenges to reliable inference. In this study, we develop an innovative model averaging framework, which leverages proximity-based matching to enhance the accuracy of HTE estimation. The method constructs pseudo-outcomes via proximity score matching and subsequently applies an optimal model averaging procedure to these matched samples. We demonstrate that the proposed estimator achieves asymptotic optimality when the standard regularity conditions are met. Simulation studies, adapted from benchmark settings for evaluating HTE model averaging, confirm its superior finite-sample performance. Compared to standard HTE estimation approaches, the proposed method achieves consistently lower estimation errors and reduced variability. The method is further validated on a clinical dataset from the CPCRA trial, demonstrating its practical value for individualized causal inference.