Causal-Driven Feature Evaluation for Cross-Domain Image Classification
This work addresses robust generalization for real-world classification tasks where test distributions differ from training data, offering a novel approach but likely incremental in the broader OOD field.
The paper tackled the problem of out-of-distribution generalization in image classification by proposing a causal-driven feature evaluation method, resulting in consistent improvements in OOD performance, particularly under challenging domain shifts.
Out-of-distribution (OOD) generalization remains a fundamental challenge in real-world classification, where test distributions often differ substantially from training data. Most existing approaches pursue domain-invariant representations, implicitly assuming that invariance implies reliability. However, features that are invariant across domains are not necessarily causally effective for prediction. In this work, we revisit OOD classification from a causal perspective and propose to evaluate learned representations based on their necessity and sufficiency under distribution shift. We introduce an explicit segment-level framework that directly measures causal effectiveness across domains, providing a more faithful criterion than invariance alone. Experiments on multi-domain benchmarks demonstrate consistent improvements in OOD performance, particularly under challenging domain shifts, highlighting the value of causal evaluation for robust generalization.