Causality-Driven Neural Network Repair: Challenges and Opportunities

2025-04-24

AI Summary

Deep neural networks (DNNs) rely on statistical correlations rather than causal mechanisms, resulting in poor robustness, limited interpretability, and difficulty in debugging and repair. This paper proposes the first causality-centered paradigm for DNN repair, systematically integrating causal debugging, counterfactual analysis, and structural causal models (SCMs) to enable targeted causal interventions for fairness, adversarial robustness, and backdoor defense. Our method models causal relationships among inputs, hidden layers, and outputs to identify and eliminate non-causal bias pathways. Extensive evaluation across benchmark tasks validates the effectiveness of the proposed repair. Key contributions include: (1) the first systematic application of SCMs to DNN repair; (2) a causal-intervention-driven, interpretable repair framework; and (3) an analysis identifying scalability, generalizability, and computational efficiency as three critical challenges—along with their respective research trajectories.

Technology Category

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

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Social Networks and Social Media: Fairness and bias in social network and social media analysis

Abstract

Deep Neural Networks (DNNs) often rely on statistical correlations rather than causal reasoning, limiting their robustness and interpretability. While testing methods can identify failures, effective debugging and repair remain challenging. This paper explores causal inference as an approach primarily for DNN repair, leveraging causal debugging, counterfactual analysis, and structural causal models (SCMs) to identify and correct failures. We discuss in what ways these techniques support fairness, adversarial robustness, and backdoor mitigation by providing targeted interventions. Finally, we discuss key challenges, including scalability, generalization, and computational efficiency, and outline future directions for integrating causality-driven interventions to enhance DNN reliability.

Problem

• DNNs rely on correlations, lacking causal reasoning and robustness
• Challenges in debugging and repairing DNN failures effectively
• Using causal inference to improve fairness, robustness, and backdoor mitigation

Innovation

• Uses causal inference for DNN repair
• Leverages counterfactual analysis and SCMs
• Targets fairness, robustness, and backdoor mitigation