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

Area of Science:

• Machine Learning
• Causal Inference
• Computer Science

Background:

• Real-world data often exhibits domain shifts due to evolving environments and selection bias, challenging traditional machine learning models.
• Existing causality-inspired methods for out-of-distribution (OOD) generalization may fail with complex spurious correlations, inadequately modeling causal intervention.
• This limitation necessitates improved methods for handling confounding variables in diverse datasets.

Purpose of the Study:

• To analyze the limitations of current methods in modeling causal intervention for OOD generalization.
• To propose a modified causal intervention approach to mitigate various types of confounders, including domain differences and spurious correlations.
• To introduce a Causal Feature Selection Module (CFSM) for robust OOD generalization.

Main Methods:

• Developed a modified causal intervention approach to address limitations in OOD generalization.
• Introduced the Causal Feature Selection Module (CFSM) to suppress model weights on domain-difference and spurious correlation features.
• Integrated CFSM within the Base-In-Sample-Cross-Sample (B-I-C) architecture for comprehensive confounding neutralization.

Main Results:

• The proposed CFSM method theoretically achieves strictly lower OOD errors under mild assumptions.
• Experimental results on benchmark datasets demonstrate the effectiveness of the CFSM method.
• The method significantly improves two-dimensional OOD generalization by mitigating both domain differences and spurious correlations.

Conclusions:

• The proposed CFSM effectively neutralizes confounding effects from domain discrepancies and correlation distinctions.
• CFSM offers a significant advancement over previous deconfounding methods by addressing hard-to-identify spurious correlations.
• This work provides a robust solution for enhancing model generalization in real-world, out-of-distribution scenarios.