A decorrelation method for general regression adjustment in randomized experiments
Abstract
We study regression adjustment with general function class approximations for estimating the average treatment effect in the design-based setting. Standard regression adjustment involves bias due to sample re-use, and this bias leads to behavior that is sub-optimal in the sample size, and/or imposes restrictive assumptions. Our main contribution is to introduce a novel decorrelation-based approach that circumvents these issues. We prove guarantees, both asymptotic and non-asymptotic, relative to the oracle functions that are targeted by a given regression adjustment procedure. We illustrate our method by applying it to various high-dimensional and non-parametric problems, exhibiting improved sample complexity and weakened assumptions relative to known approaches.
Cite
@article{arxiv.2311.10076,
title = {A decorrelation method for general regression adjustment in randomized experiments},
author = {Fangzhou Su and Wenlong Mou and Peng Ding and Martin J. Wainwright},
journal= {arXiv preprint arXiv:2311.10076},
year = {2023}
}
Comments
Fangzhou Su and Wenlong Mou contributed equally to this work