Summary of Root Cause Analysis Of Outliers with Missing Structural Knowledge, by Nastaran Okati et al.
Root Cause Analysis of Outliers with Missing Structural Knowledge
by Nastaran Okati, Sergio Hernan Garrido Mejia, William Roy Orchard, Patrick Blöbaum, Dominik Janzing
First submitted to arxiv on: 7 Jun 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 This paper extends recent work on root cause analysis (RCA) of anomalies by conceptualizing quantitative contribution analysis using causal counterfactuals in structural causal models (SCMs). The framework faces three practical challenges: requiring a causal directed acyclic graph (DAG) and SCM, statistical ill-posedness due to probing regression models in low probability density regions, and reliance on computationally expensive Shapley values. To address these limitations, the authors propose [insert proposed solutions or future work]. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper takes the concept of root cause analysis one step further by using causal counterfactuals in structural causal models (SCMs) to analyze anomalies. The method has some challenges, like needing a special kind of graph and SCM, not working well in certain situations, and being slow to compute. This research aims to make this method more practical for real-world applications. |
Keywords
* Artificial intelligence * Probability * Regression
