CauRDG: Enhancing Domain Generalization with Causal-Driven Semantic Consistency Reasoning

Domain generalization (DG) plays a pivotal role in enabling models to maintain robust performance across heterogeneous environments. However, existing DG methods are fundamentally constrained by two intertwined limitations: (1) causal misalignment, which stems from undifferentiated feature encoding that entangles causal mechanisms with environmental biases; (2)semantic conflict arises when conventional adaptation methods find it challenging to balance the preservation of class discriminability with the mitigation of domain-specific distribution discrepancies. To address these challenges of DG, we propose a novel Causal-Driven Semantic Consistency Reasoning (CauRDG) method, which synergistically integrates Prototype-Guided Causal Disentanglement (PGCD) and Dual-Space Semantic Disambiguation (DSSD). Specifically, PGCD constructs a causal framework that identifies stable relationships and decouples invariant mechanisms from domain-specific variations, preserving causal consistency while adapting to contextual differences. DSSD harnesses a dual-space paradigm, enhancing local categorical clarity and maintaining global conceptual unity, thus balancing domain-specific precision with cross-domain coherence. The robustness provided by CauRDG ensures robust extraction and interpretation of essential features by preserving invariant causal structures, thereby harmonizing discriminative semantics with domain-varying contexts. Extensive experiments on multiple benchmark datasets consistently demonstrate the effectiveness and superiority of our CauRDG over state-of-the-art baselines.