Summary of Causal-discovery-based Root-cause Analysis and Its Application in Time-series Prediction Error Diagnosis, by Hiroshi Yokoyama et al.
Causal-discovery-based root-cause analysis and its application in time-series prediction error diagnosis
by Hiroshi Yokoyama, Ryusei Shingaki, Kaneharu Nishino, Shohei Shimizu, Thong Pham
First submitted to arxiv on: 11 Nov 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Causal-Discovery-based Root-Cause Analysis (CD-RCA) method estimates causal relationships between prediction error and explanatory variables without requiring a predefined causal graph. This approach outperforms current heuristic attribution methods in simulations and can identify variable contributions to outliers in prediction errors by Shapley values. The paper aims to improve the accuracy of error attribution in machine learning models, particularly in industrial applications where transparency is crucial. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning has made significant progress in predicting models, but these models are often “black boxes” making it difficult to diagnose prediction errors, especially with outliers. Current methods can’t accurately identify why errors happen or which variables contribute to them. The new CD-RCA method changes this by figuring out how variables cause errors without needing a map beforehand. This makes it better at finding the real causes of errors. |
Keywords
* Artificial intelligence * Machine learning
