An adaptive variance estimator for relative sparsity

TL;DR

This paper introduces a new variance estimator for policy coefficients in relative sparsity models, enhancing uncertainty quantification and supporting safer policy learning in clinical settings.

Contribution

It develops a variance estimator that fully utilizes an existing asymptotic normality theorem, improving inference accuracy in policy learning.

Findings

Improved variance estimation for policy coefficients.

Enhanced uncertainty representation in graphical selection.

Facilitates safer policy learning in clinical medicine.

Abstract

An approach to inference for relative sparsity was developed in prior work, and an adaptive lasso asymptotic normality theorem was given there, but this theorem was not fully used when estimating the variance of the policy coefficients. Here, we develop a new coefficient variance estimator that fully uses this theorem and, in the process, takes into account the variable selection. This improves the uncertainty representation in the graphical selection diagrams, ultimately facilitating the safe use of policy learning in clinical medicine.