[2507.21783] Domain Generalization and Adaptation in Intensive Care with Anchor Regression

Statistics > Applications arXiv:2507.21783 (stat) [Submitted on 29 Jul 2025 (v1), last revised 28 Feb 2026 (this version, v2)] Title:Domain Generalization and Adaptation in Intensive Care with Anchor Regression Authors:Malte Londschien, Manuel Burger, Gunnar Rätsch, Peter Bühlmann View a PDF of the paper titled Domain Generalization and Adaptation in Intensive Care with Anchor Regression, by Malte Londschien and 3 other authors View PDF HTML (experimental) Abstract:The performance of predictive models in clinical settings often degrades when deployed in new hospitals due to distribution shifts. This paper presents a large-scale study of causality-inspired domain generalization on heterogeneous multi-center intensive care unit (ICU) data. We apply anchor regression and introduce anchor boosting, a novel, tree-based nonlinear extension, to a large dataset comprising 400,000 patients from nine distinct ICU databases. We find that anchor regularization yields improvements of out-of-distribution performance, particularly for the most dissimilar target domains. The methods appear robust to violations of theoretical assumptions, such as anchor exogeneity. Furthermore, we propose a novel conceptual framework to quantify the utility of large external data datasets. By evaluating performance as a function of available target-domain data, we identify three regimes: (i) a domain generalization regime, where only the external model should be used, (ii) a domain adaptation regime, where...