Irrelevant Variable

Irrelevant variables pose a significant challenge across diverse fields, hindering accurate model building and inference. Current research focuses on developing methods to identify and mitigate the influence of these variables, particularly within complex models like deep neural networks and large language models, employing techniques such as deep disentanglement and knockoff filtering to improve variable selection and prediction accuracy. This work is crucial for enhancing the reliability and interpretability of models in various applications, from medical treatment effect estimation to image generation and natural language processing, ultimately leading to more robust and trustworthy scientific findings.