Estimating Conditional Average Treatment Effects with Heteroscedasticity by Model Averaging and Matching
Methodology
2024-12-17 v2 Applications
Abstract
We propose a model averaging approach, combined with a partition and matching method to estimate the conditional average treatment effects under heteroskedastic error settings. The proposed approach has asymptotic optimality and consistency of weights and estimator. Numerical studies show that our method has good finite-sample performances.
Cite
@article{arxiv.2304.06960,
title = {Estimating Conditional Average Treatment Effects with Heteroscedasticity by Model Averaging and Matching},
author = {Pengfei Shi and Xinyu Zhang and Wei Zhong},
journal= {arXiv preprint arXiv:2304.06960},
year = {2024}
}