Causal-discovery-based root-cause analysis and its application in time-series prediction error diagnosis
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 experiments show CD-RCA outperforms current heuristic attribution methods.
Code (1)
Tasks
Causal DiscoveryPredictionTime SeriesTime Series PredictionSimilar Papers 제목 키워드 기반
Root Cause Analysis In Microservice Using Neural Granger Causal Discovery
In recent years, microservices have gained widespread adoption in IT operations due to their scalability, maintenance, and flexibility. However, it becomes challenging for site reliability engineers (SREs) to pinpoint th…
Causal DiscoveryContrastive LearningCPUTime Series+1Root cause analysis via difference graph discovery from linear time-series data
Root cause analysis aims to identify the mechanisms responsible for anomalies in complex dynamical systems. In this paper, we study root cause analysis in linear time-series through the lens of difference graph discovery…
Hierarchical Graph Neural Networks for Causal Discovery and Root Cause Localization
In this paper, we propose REASON, a novel framework that enables the automatic discovery of both intra-level (i.e., within-network) and inter-level (i.e., across-network) causal relationships for root cause localization.…
Causal DiscoveryTime SeriesTime Series AnalysisPyRCA: A Library for Metric-based Root Cause Analysis
We introduce PyRCA, an open-source Python machine learning library of Root Cause Analysis (RCA) for Artificial Intelligence for IT Operations (AIOps). It provides a holistic framework to uncover the complicated metric ca…
Causal Discoverygraph constructionAnwendung von Causal-Discovery-Algorithmen zur Root-Cause-Analyse in der Fahrzeugmontage
Root Cause Analysis (RCA) is a quality management method that aims to systematically investigate and identify the cause-and-effect relationships of problems and their underlying causes. Traditional methods are based on t…
Causal DiscoveryManagement