提出CENNET方法，用因果模型解释神经网络在表格数据上的预测结果。
A Model of Causal Explanation on Neural Networks for Tabular Data
- 结合结构因果模型与神经网络，实现对表格数据预测的因果解释。
- 在合成和准真实数据上，相比现有方法提升解释可靠性。
- 适合关注模型可解释性与因果推理的研究者和工程师。
机器学习结果的解释问题持续受到关注。神经网络（NN）与梯度提升机在表格数据上表现出高预测精度，但其解释性仍面临伪相关、因果关系及组合原因等挑战。本文提出一种因果解释方法CENNET，并引入基于熵的解释力新指标。CENNET通过有效融合结构因果模型（SCMs）与神经网络，在不牺牲预测准确性的前提下，为神经网络提供因果解释。在分类任务中，通过对合成数据与准真实数据的对比实验，验证了该方法在解释质量上的优越性。
原文摘要 · Abstract (English)
The problem of explaining the results produced by machine learning methods continues to attract attention. Neural network (NN) models, along with gradient boosting machines, are expected to be utilized even in tabular data with high prediction accuracy. This study addresses the related issues of pseudo-correlation, causality, and combinatorial reasons for tabular data in NN predictors. We propose a causal explanation method, CENNET, and a new explanation power index using entropy for the method. CENNET provides causal explanations for predictions by NNs and uses structural causal models (SCMs) effectively combined with the NNs although SCMs are usually not used as predictive models on their own in terms of predictive accuracy. We show that CEN-NET provides such explanations through comparative experiments with existing methods on both synthetic and quasi-real data in classification tasks.
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