As a key problem and hub link between problem discovery and problem solving, the current research on root cause analysis mainly includes two types of methods: data-driven and causality-driven. In view of the advantages of data-driven methods in narrowing the range of root causes, at present, root cause research mainly focusses on data-driven methods such as association rule mining, heuristic search, machine learning and deep learning. There are few root cause analysis from the perspective of causal knowledge, nor has it been based on method dimension. The inductive analysis of the root cause is carried out, and there is a lack of relevant research results. Therefore, this paper sorted out and summarized the main results of root cause analysis in recent years, analysed the differences and advantages of root cause analysis under different method dimensions, and proposed a root cause analysis method that integrated causal knowledge, and combined asymmetric Shapley value with causal chain diagram to improve the accuracy of root cause analysis. Finally, the existing research difficulties and development trends put forward meaningful future research directions.

[2022-11-14] Accepted Paper /doc/2022.07.0450/v1

[2023-04-05] Printed Article /doc/2022.07.0450/v2