Root Cause Validation via Inverse Causation Probability
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Solution Overview
Problem
Current root cause analysis methods often misdirect investigations and lead to incorrect or incomplete resolutions due to a lack of sufficient causal links between answers in the sequential questioning process, resulting in potential misidentification of the root cause.
Innovation Solution
A method and system that utilize reverse causation analysis to validate the identified root cause by determining the probability of causation between consecutive answers in a sequence, employing a Q&A system trained on the subject-matter domain to assess the likelihood of causal relationships and trigger review or re-analysis workflows as necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If sequential questioning is used to identify root cause, then analysis depth is improved, but accuracy of root cause identification deteriorates due to lack of causal link validation
Solution Approach 1:
The system implements feedback by using the Q&A system to evaluate whether each answer in the sequence is causally linked to the next question. The causation probability scores provide feedback on the validity of the analysis path, allowing the system to identify and correct weak causal links that would otherwise lead to incorrect root cause identification.
Solution Approach 2:
Instead of only moving forward from problem to potential root cause through sequential questioning, the system applies inverse causation analysis by using a Q&A system to evaluate whether each answer would logically cause the next question. This reverse validation ensures that the causal relationships are genuine and not merely sequential coincidences.
2Measurement precision
If automated Q&A system is used to determine causation probability, then validation accuracy is improved, but system complexity increases
Solution Approach 1:
The system introduces a Q&A system as an intermediary component that specializes in evaluating causal relationships within the specific domain. This intermediary handles the complex task of causation probability assessment, allowing the main root cause analysis system to remain relatively simple while achieving high validation accuracy through the specialized intermediary.
3Measurement precision
If review workflow is triggered for low probability answer pairs, then root cause accuracy is improved, but analysis time increases
Solution Approach 1:
Instead of reviewing all answer pairs in the sequence, the system applies partial action by triggering review workflows only for answer pairs with causation probability below a predetermined threshold. This selective approach maintains root cause accuracy by focusing review efforts on suspicious cases while avoiding unnecessary review of clearly valid causal links, thus minimizing time loss.
Data Source
AI summary
From a sequence of answers, a last remaining answer is selected. A set of answers in the sequence of answers are responsive to a set of questions resolved during an analysis of a reported problem in a data processing environment. An answer pair is formed using the last remaining answer and another answer which immediately precedes the last remaining answer in the sequence of answers. A probability is determined of the last remaining answer being caused by the other answer in the answer pair. When the probability is below a threshold value, a review workflow is triggered corresponding to a portion of the analysis. The portion includes a question corresponding to an answer in the answer pair.