Causal-Driven Feature Evaluation for Cross-Domain Image Classification

2026-01-28

AI Summary

This work addresses the performance degradation in out-of-distribution (OOD) generalization caused by significant shifts between training and test distributions. Departing from conventional reliance on domain invariance assumptions, the paper proposes a causality-inspired feature evaluation framework that introduces, for the first time, causal necessity and sufficiency as criteria to assess the effectiveness of cross-domain representations. By conducting segment-wise causal analysis, the method directly quantifies the causal effects of features across domains. Extensive experiments on multiple cross-domain image classification benchmarks demonstrate its superiority under severe distribution shifts, achieving substantially more reliable OOD generalization performance compared to existing approaches.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

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.

Problem

• out-of-distribution generalization
• causal evaluation
• domain shift
• feature invariance
• cross-domain classification

Innovation

• causal evaluation
• necessity and sufficiency
• out-of-distribution generalization
• domain shift
• feature representation