Deconfounded and debiased estimation for high-dimensional linear regression under hidden confounding with application to omics data.

ABSTRACT:

Motivation

A critical challenge in observational studies arises from the presence of hidden confounders in high-dimensional data. This leads to biases in causal effect estimation due to both hidden confounding and high-dimensional estimation. Some classical deconfounding methods are inadequate for high-dimensional scenarios and typically require prior information on hidden confounders. We propose a two-step deconfounded and debiased estimation for high-dimensional linear regression with hidden confounding.

Results

First, we reduce hidden confounding via spectral transformation. Second, we correct bias from the weighted ℓ1 penalty, commonly used in high-dimensional estimation, by inverting the Karush-Kuhn-Tucker conditions and solving convex optimization programs. This deconf

SUBMITTER: Li Z 

PROVIDER: S-EPMC12381636 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature