Causality-Driven Neural Network Repair: Challenges and Opportunities

TL;DR

The paper addresses the fragility of Deep Neural Networks arising from reliance on statistical correlations rather than causal mechanisms. It advocates a causality-driven repair framework, leveraging feature-level and neuron-level interventions, counterfactual analysis, and Structural Causal Models to diagnose and correct failures, with demonstrated potential benefits for fairness and robustness. It surveys existing methods (e.g., de-confounded training, ACE, PSO-based neuron repair, CausalAdv) and outlines key challenges—scalability, high-dimensional causal discovery, optimization trade-offs, benchmarks, and integration with architectures. The work argues for a shift toward causality-aware DL that improves reliability in safety-critical domains, emphasizing scalable discovery, modular architecture integration, and standardized evaluation to enable practical deployment.

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.