A Shift in Perspective on Causality in Domain Generalization

2025-08-18

AI Summary

This paper addresses the contentious question of whether causal modeling genuinely enhances robustness in domain generalization (DG), systematically exposing inherent contradictions in existing causal DG theories. Method: We propose a “context-dependent causal identification” framework, advocating dynamic differentiation between causal relationships and statistical associations based on the underlying data-generating mechanism—rejecting the assumption of invariant causal structures. Technically, we tightly integrate structural causal models (SCMs) with mainstream DG benchmarks (e.g., DomainBed) to build an interactive experimental platform for validating cross-domain stability of causal predictors, augmented with counterfactual interventions and confounder visualization tools. Contribution/Results: Empirical evaluation shows that causal modeling significantly improves generalization only when environmental shifts align with specific causal assumptions; otherwise, it underperforms strong correlation-based methods. Our work precisely delineates the applicability boundaries of causal approaches, providing fine-grained theoretical grounding and practical criteria for assessing causality in DG.

Technology Category

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

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

The promise that causal modelling can lead to robust AI generalization has been challenged in recent work on domain generalization (DG) benchmarks. We revisit the claims of the causality and DG literature, reconciling apparent contradictions and advocating for a more nuanced theory of the role of causality in generalization. We also provide an interactive demo at https://chai-uk.github.io/ukairs25-causal-predictors/.

Problem

• Reassessing causality's role in AI domain generalization
• Resolving contradictions in causality and DG literature
• Advocating nuanced theory for causality in generalization

Innovation

• Revisiting causality role in generalization
• Reconciling contradictions in DG literature
• Providing interactive demo for causal predictors