Neural Networks Decoded: Targeted and Robust Analysis of Neural Network Decisions via Causal Explanations and Reasoning
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解码神经网络：基于因果解释与推理的针对性稳健分析
- 研究问题
- 如何通过输入干预解释深度神经网络的内部决策过程，识别重要特征和可能偏差，并提供有结构的反事实解释？
- 方法与思路
- 作者提出 TRACER，干预输入并追踪内部激活与输出变化，将功能相似层组合为因果节点，再构建因果图并生成反事实解释。
- 材料陈述的贡献
- 摘要称该方法无需改变原网络架构即可解释决策，在多数据集比较中表现优于现有方法，并展示用于高压缩模型的潜力。
- 阅读关注点
- 应核对干预和因果效应的定义、输入变化是否合理、层分组依据，以及解释忠实度、反事实质量和压缩后准确率的评价。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收均未核实；网络内部干预不自动识别现实因果关系，摘要未说明比较细节和性能边界。
原文摘要
Despite their success and widespread adoption, the opaque nature of deep neural networks (DNNs) continues to hinder trust, especially in critical applications. Current interpretability solutions often yield inconsistent or oversimplified explanations, or require model changes that compromise performance. In this work, we introduce TRACER, a novel method grounded in causal inference theory designed to estimate the causal dynamics underpinning DNN decisions without altering their architecture or compromising their performance. Our approach systematically intervenes on input features to observe how specific changes propagate through the network, affecting internal activations and final outputs. Based on this analysis, we determine the importance of individual features, and construct a high-level causal map by grouping functionally similar layers into cohesive causal nodes, providing a structured and interpretable view of how different parts of the network influence the decisions. TRACER further enhances explainability by generating counterfactuals that reveal possible model biases and offer contrastive explanations for misclassifications. Through comprehensive evaluations across diverse datasets, we demonstrate TRACER's effectiveness over existing methods and show its potential for creating highly compressed yet accurate models, illustrating its dual versatility in both understanding and optimizing DNNs.