Auxiliary Covariates

Auxiliary covariates (ACs) are supplementary variables incorporated into statistical models to improve estimation accuracy and address challenges like missing data or confounding. Current research focuses on leveraging ACs within various frameworks, including multiple imputation, deep learning models (e.g., neural networks), and information bottleneck methods, often employing techniques like LASSO for variable selection or semi-definite programming for optimization. The effective use of ACs enhances the power and reliability of statistical inferences across diverse fields, from healthcare studies addressing missing confounders to improving soil fertility prediction and natural language processing evaluations.