Causality-Driven Neural Network Repair: Challenges and Opportunities

• URL: http://arxiv.org/abs/2504.17946v1
• Date: Thu, 24 Apr 2025 21:22:00 GMT
• Title: Causality-Driven Neural Network Repair: Challenges and Opportunities
• Authors: Fatemeh Vares, Brittany Johnson,
• Abstract summary: Deep Neural Networks (DNNs) often rely on statistical correlations rather than causal reasoning, limiting their robustness and interpretability.<n>This paper explores causal inference as an approach primarily for DNN repair, leveraging causal debug, and structural causal models (SCMs) to identify and correct failures.
• Score: 5.69361786082969
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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