Causal-discovery-based root-cause analysis and its application in time-series prediction error diagnosis

• URL: http://arxiv.org/abs/2411.06990v1
• Date: Mon, 11 Nov 2024 13:48:13 GMT
• Title: Causal-discovery-based root-cause analysis and its application in time-series prediction error diagnosis
• Authors: Hiroshi Yokoyama, Ryusei Shingaki, Kaneharu Nishino, Shohei Shimizu, Thong Pham,
• Abstract summary: Heuristic attribution methods, while helpful, often fail to capture true causal relationships, leading to inaccurate error attributions. We introduce the Causal-Discovery-based Root-Cause Analysis (CD-RCA) method that estimates causal relationships between the prediction error and the explanatory variables. By simulating synthetic error data, CD-RCA can identify variable contributions to outliers in prediction errors by Shapley values.
• Score: 8.309366167066278
• License:
• Abstract: 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 simulations show CD-RCA outperforms current heuristic attribution methods, and a sensitivity analysis reveals new patterns where Shapley values may misattribute errors, paving the way for more accurate error attribution methods.

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