The oracle property of the generalized outcome adaptive lasso
Statistics Theory
2024-06-11 v2 Methodology
Statistics Theory
Abstract
The generalized outcome-adaptive lasso (GOAL) is a variable selection for high-dimensional causal inference proposed by Bald\'e et al. [2023, {\em Biometrics} {\bfseries 79(1)}, 514--520]. When the dimension is high, it is now well established that an ideal variable selection method should have the oracle property to ensure the optimal large sample performance. However, the oracle property of GOAL has not been proven. In this paper, we show that the GOAL estimator enjoys the oracle property. Our simulation shows that the GOAL method deals with the collinearity problem better than the oracle-like method, the outcome-adaptive lasso (OAL).
Keywords
Cite
@article{arxiv.2310.00250,
title = {The oracle property of the generalized outcome adaptive lasso},
author = {Ismaila Baldé},
journal= {arXiv preprint arXiv:2310.00250},
year = {2024}
}