无需预设因果图，用因果发现定位预测误差根源
Causal-discovery-based root-cause analysis and its application in time-series prediction error diagnosis
- 基于因果发现自动推导误差与变量的因果关系
- 在合成数据上准确识别出异常误差的主要贡献变量
- 适合需要可解释性的工业级预测模型故障诊断
机器学习模型预测精度虽大幅提升，但多数仍为“黑箱”，尤其在异常值情况下难以进行误差诊断，影响工业应用中的可信度。现有启发式归因方法常无法捕捉真实因果关系，导致误判。尽管已有基于Shapley值的根因分析方法，但通常依赖预设因果图，适用性受限。为此，本文提出基于因果发现的根因分析（CD-RCA）方法，无需预定义因果图即可估计预测误差与解释变量间的因果关系。通过模拟合成误差数据，CD-RCA利用Shapley值量化各变量对异常误差的贡献。大量实验表明，该方法显著优于现有启发式归因方法。
原文摘要 · Abstract (English)
Recent rapid advancements of machine learning have greatly enhanced the accuracy of prediction models, but most models remain "black boxes", making prediction error diagnosis challenging, especially with outliers. This lack of transparency hinders trust and reliability in industrial applications. Heuristic attribution methods, while helpful, often fail to capture true causal relationships, leading to inaccurate error attributions. Various root-cause analysis methods have been developed using Shapley values, yet they typically require predefined causal graphs, limiting their applicability for prediction errors in machine learning models. To address these limitations, we introduce the Causal-Discovery-based Root-Cause Analysis (CD-RCA) method that estimates causal relationships between the prediction error and the explanatory variables, without needing a pre-defined causal graph. By simulating synthetic error data, CD-RCA can identify variable contributions to outliers in prediction errors by Shapley values. Extensive experiments show CD-RCA outperforms current heuristic attribution methods.
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