CFSM: A Novel Causal Feature Selection Module for Two-Dimensional Out-of-Distribution Generalization

In real-world scenarios, training and test data are often collected in diverse settings, leading to domain shifts arising from evolving environments and selection bias. While causality-inspired methods have shown promising results in tackling the out-of-distribution (OOD) generalization issue, prior methods treat the discovered differences across domains as confounding variables. While effective in handling domain differences (i.e., unseen environmental features in test data), they may fail when