Neural Causal Graph for Interpretable and Intervenable Classification
Jiawei Wang Shaofei Lu Da Cao Dongyu Wang Yuquan Le Zhe Quan Tat-Seng Chua
Advancements in neural networks have significantly enhanced the performance of classification models, achieving remarkable accuracy across diverse datasets. However, these models often lack transparency and do not support interactive reasoning with human users, which are essential attributes for applications that require trust and user engagement. To overcome these limitations, we introduce an innovative framework, Neural Causal Graph (NCG), that integrates causal inference with neural networks to enable interpretable and intervenable reasoning. We then propose an intervention training method to model the intervention probability of the prediction, serving as a contextual prompt to facilitate the fine-grained reasoning and human-AI interaction abilities of NCG. Our experiments show that the proposed framework significantly enhances the performance of traditional classification baselines. Furthermore, NCG achieves nearly 95\% top-1 accuracy on the ImageNet dataset by employing a test-time intervention method. This framework not only supports sophisticated post-hoc interpretation but also enables dynamic human-AI interactions, significantly improving the model's transparency and applicability in real-world scenarios.
Causal Inference, Graph Neural Network, Classification
NCG combines causal inference with neural networks to enhance interpretability and human-AI interaction. It outperforms traditional baselines with a unique intervention training method, achieving up to 95% top-1 accuracy on the ImageNet dataset.