PORCA: Root Cause Analysis with Partially Observed Data

• URL: http://arxiv.org/abs/2407.05869v2
• Date: Fri, 12 Jul 2024 01:28:49 GMT
• Title: PORCA: Root Cause Analysis with Partially Observed Data
• Authors: Chang Gong, Di Yao, Jin Wang, Wenbin Li, Lanting Fang, Yongtao Xie, Kaiyu Feng, Peng Han, Jingping Bi,
• Abstract summary: Root Cause Analysis (RCA) aims at identifying the underlying causes of system faults by uncovering and analyzing the causal structure from complex systems. Previous studies implicitly assume a full observation of the system, which neglect the effect of partial observation. We propose PORCA, a novel RCA framework which can explore reliable root causes under both unobserved confounders and unobserved heterogeneity.
• Score: 15.007249208547885
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Root Cause Analysis (RCA) aims at identifying the underlying causes of system faults by uncovering and analyzing the causal structure from complex systems. It has been widely used in many application domains. Reliable diagnostic conclusions are of great importance in mitigating system failures and financial losses. However, previous studies implicitly assume a full observation of the system, which neglect the effect of partial observation (i.e., missing nodes and latent malfunction). As a result, they fail in deriving reliable RCA results. In this paper, we unveil the issues of unobserved confounders and heterogeneity in partial observation and come up with a new problem of root cause analysis with partially observed data. To achieve this, we propose PORCA, a novel RCA framework which can explore reliable root causes under both unobserved confounders and unobserved heterogeneity. PORCA leverages magnified score-based causal discovery to efficiently optimize acyclic directed mixed graph under unobserved confounders. In addition, we also develop a heterogeneity-aware scheduling strategy to provide adaptive sample weights. Extensive experimental results on one synthetic and two real-world datasets demonstrate the effectiveness and superiority of the proposed framework.

Related papers

• Root Cause Analysis of Outliers with Missing Structural Knowledge [13.152501863685478]
  We propose simplified, efficient methods of root cause analysis when the task is to identify a unique root cause instead of quantitative contribution analysis. Our proposed methods run in linear order of SCM nodes and they require only the causal DAG without counterfactuals. We prove that anomalies with small scores are unlikely to cause those with large scores and show upper bounds for the likelihood of causal pathways with non-monotonic anomaly scores.
  arXiv  Detail & Related papers  (2024-06-07T15:24:38Z)
• Weakly-supervised causal discovery based on fuzzy knowledge and complex data complementarity [4.637772575470497]
  We propose a novel weakly-supervised fuzzy knowledge and data co-driven causal discovery method named KEEL. KEEL adopts a fuzzy causal knowledge schema to encapsulate diverse types of fuzzy knowledge, and forms corresponding weakened constraints. It can enhance the generalization and robustness of causal discovery, especially in high-dimensional and small-sample scenarios.
  arXiv  Detail & Related papers  (2024-05-14T15:39:22Z)
• Multi-modal Causal Structure Learning and Root Cause Analysis [67.67578590390907]
  We propose Mulan, a unified multi-modal causal structure learning method for root cause localization. We leverage a log-tailored language model to facilitate log representation learning, converting log sequences into time-series data. We also introduce a novel key performance indicator-aware attention mechanism for assessing modality reliability and co-learning a final causal graph.
  arXiv  Detail & Related papers  (2024-02-04T05:50:38Z)
• Discovering Dynamic Causal Space for DAG Structure Learning [64.763763417533]
  We propose a dynamic causal space for DAG structure learning, coined CASPER. It integrates the graph structure into the score function as a new measure in the causal space to faithfully reflect the causal distance between estimated and ground truth DAG.
  arXiv  Detail & Related papers  (2023-06-05T12:20:40Z)
• Nonparametric Identifiability of Causal Representations from Unknown Interventions [63.1354734978244]
  We study causal representation learning, the task of inferring latent causal variables and their causal relations from mixtures of the variables. Our goal is to identify both the ground truth latents and their causal graph up to a set of ambiguities which we show to be irresolvable from interventional data.
  arXiv  Detail & Related papers  (2023-06-01T10:51:58Z)
• Hierarchical Graph Neural Networks for Causal Discovery and Root Cause Localization [52.72490784720227]
  REASON consists of Topological Causal Discovery and Individual Causal Discovery. The Topological Causal Discovery component aims to model the fault propagation in order to trace back to the root causes. The Individual Causal Discovery component focuses on capturing abrupt change patterns of a single system entity.
  arXiv  Detail & Related papers  (2023-02-03T20:17:45Z)
• Causality-Based Multivariate Time Series Anomaly Detection [63.799474860969156]
  We formulate the anomaly detection problem from a causal perspective and view anomalies as instances that do not follow the regular causal mechanism to generate the multivariate data. We then propose a causality-based anomaly detection approach, which first learns the causal structure from data and then infers whether an instance is an anomaly relative to the local causal mechanism. We evaluate our approach with both simulated and public datasets as well as a case study on real-world AIOps applications.
  arXiv  Detail & Related papers  (2022-06-30T06:00:13Z)
• Causal Inference-Based Root Cause Analysis for Online Service Systems with Intervention Recognition [11.067832313491449]
  In this paper, we formulate the root cause analysis problem as a new causal inference task named intervention recognition. We propose a novel unsupervised causal inference-based method named Causal Inference-based Root Cause Analysis (CIRCA) The performance on a real-world dataset shows that CIRCA can improve the recall of the top-1 recommendation by 25% over the best baseline method.
  arXiv  Detail & Related papers  (2022-06-13T01:45:13Z)
• ACRE: Abstract Causal REasoning Beyond Covariation [90.99059920286484]
  We introduce the Abstract Causal REasoning dataset for systematic evaluation of current vision systems in causal induction. Motivated by the stream of research on causal discovery in Blicket experiments, we query a visual reasoning system with the following four types of questions in either an independent scenario or an interventional scenario. We notice that pure neural models tend towards an associative strategy under their chance-level performance, whereas neuro-symbolic combinations struggle in backward-blocking reasoning.
  arXiv  Detail & Related papers  (2021-03-26T02:42:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.