Evaluation of Causal Inference Techniques for AIOps
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Summary
Preliminary results indicate that event models yield causal graphs that have high precision and recall in comparison to regression and independence testing based Granger methods.
- Type
- article
- Published
- 2020-12-27
- Cited by
- 19
- References
- 33
- OpenAlex
- https://openalex.org/W3116539649
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:229548839
Keywords
Causal inference, Benchmark (surveying), Inference, Computer science, Event (particle physics)
References
- GRAPHICAL MODELLING OF MULTIVARIATE TIME SERIES WITH LATENT VARIABLES
- Pattern recognition
- An Algorithm for Fast Recovery of Sparse Causal Graphs
- The Bayesian Lasso
- Grouped graphical Granger modeling methods for temporal causal modeling
- Measures of Conditional Linear Dependence and Feedback between Time Series
- The MVGC multivariate Granger causality toolbox: a new approach to Granger-causal inference.
- Temporal causal modeling with graphical granger methods
- Directed Information Graphs
- Causal relation of queries from temporal logs
- The max-min hill-climbing Bayesian network structure learning algorithm
- Causation, Prediction, and Search, 2nd Edition
- Universal Models of Multivariate Temporal Point Processes
- Finding Needles in the Haystack: Harnessing Syslogs for Data Center Management
- Approximate Kernel-Based Conditional Independence Tests for Fast Non-Parametric Causal Discovery
- DeepLog: Anomaly Detection and Diagnosis from System Logs through Deep Learning
- Mining Causality of Network Events in Log Data
- Neural Granger Causality for Nonlinear Time Series
- Structure Learning from Time Series with False Discovery Control
- An unsupervised framework for detecting anomalous messages from syslog log files
Cited by
- Anomaly Detection and Failure Root Cause Analysis in (Micro) Service-Based Cloud Applications: A Survey
- Detecting Causal Structure on Cloud Application Microservices Using Granger Causality Models
- Comparative Causal Analysis of Network Log Data in Two Large ISPs
- Toward explainable deep learning
- Applications of statistical causal inference in software engineering
- Applications of Causality and Causal Inference in Software Engineering
- Blueprint: A Toolchain for Highly-Reconfigurable Microservice Applications
- Machine learning and feature selection: Applications in economics and climate change
- FIRED: a fine-grained robust performance diagnosis framework for cloud applications
- RealTCD: Temporal Causal Discovery from Interventional Data with Large Language Model
- Root Cause Analysis for Microservices based on Causal Inference: How Far Are We?
- NICSDG: A Non-Intrusive Approach to Constructing Concise Service Dependency Graphs for Microservice Systems
- Failure Diagnosis in Microservice Systems: A Comprehensive Survey and Analysis
- LogAn: An LLM-Based Log Analytics Tool with Causal Inferencing
- Intelligent Root Cause Localization in MicroService Systems: A Survey and New Perspectives
- NetEventCause: Event-Driven Root Cause Analysis for Large Network System Without Topology
- LEMAD: LLM-Empowered Multi-Agent System for Anomaly Detection in Power Grid Services
- Causal Inference for Event Pairs in Multivariate Point Processes
- Adaptive Anomaly Detection for Multi-Dimensional Issue Reports
- Observability-Debt-Shielded AIOps for Regulated Finance and Energy Systems: Telemetry-Parity Incident Triage and SLO Risk Scoring
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