StableRCA: Robust Graph-Agnostic Mechanism-Level Root Cause Analysis

• URL: http://arxiv.org/abs/2606.05636v1
• Date: Thu, 04 Jun 2026 03:02:23 GMT
• Title: StableRCA: Robust Graph-Agnostic Mechanism-Level Root Cause Analysis
• Abstract summary: Root-Cause Analysis (RCA) seeks to identify the variables responsible for abnormal system behavior in complex domains such as manufacturing, cloud computing, and healthcare.<n>We propose StableRCA, a local mechanism-level RCA framework that avoids global graph discovery by estimating local Markov boundaries and detecting conditional distribution shifts within them.<n> Experiments on synthetic benchmarks and five real-world datasets demonstrate that StableRCA is robust to graph misspecification, effective under multiple intervention targets, scalable to large systems, and reliable across diverse application domains.
• Score: 4.9565773787819385
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Root-Cause Analysis (RCA) seeks to identify the variables responsible for abnormal system behavior in complex domains such as manufacturing, cloud computing, and healthcare. Existing approaches face a critical bottleneck: graph-based causal methods can identify intervention targets but typically require a known or accurately estimated causal graph, while graph-free statistical methods either localize marginal anomalies rather than structural causes, or rely on restrictive assumptions about graph structure or functional form. We propose StableRCA, a local mechanism-level RCA framework that avoids global graph discovery by estimating local Markov boundaries and detecting conditional distribution shifts within them. Leveraging the Independent Causal Mechanism principle, we show that intervention targets can be identified with probability converging exponentially in sample size under faithful Markov boundary recovery and non-degenerate mechanism shifts. Experiments on synthetic benchmarks and five real-world datasets demonstrate that StableRCA is robust to graph misspecification, effective under multiple intervention targets, scalable to large systems, and reliable across diverse application domains. Code is available at: https://anonymous.4open.science/r/StableRCA-E362

Related papers

• Root Cause Analysis with Latent Confounders using Partial Ancestral Graphs [0.8602553195689513]
  Finding the source of failures is essential for identifying the root causes of anomalies and maintaining the reliability of complex systems.<n>While causal theory has advanced data-driven Root Cause Analysis, existing frameworks assume causal sufficiency, failing to account for the unobserved latent variables prevalent in real-world environments.<n>This framework models system failures as parametric interventions over Partial Ancestral Graphs (PAGs) to perform RCA in the presence of latent variables.
  arXiv  Detail & Related papers  (2026-06-18T20:08:34Z)
• Causal Ensemble Agent: Hierarchical Causal Discovery with LLM-guided Expert Reweighting [95.24017293537025]
  Causal Ensemble Agent (CEA) is a novel framework that aggregates structural insights from statistical discovery experts across different graph levels.<n>CEA achieves the strongest overall performance across a wide range of causal discovery methods.
  arXiv  Detail & Related papers  (2026-06-09T09:09:07Z)
• FedCIGAR: A Personalized Reconstruction Approach for Federated Graph-level Anomaly Detection [51.473064583839296]
  We propose a novel Federated graph-level anomaly detection approach with Cluster-adaptIve GAted Reconstruction (FedCIGAR)<n>Specifically, we design a reconstruction-based paradigm trained on normal graphs to avoid synthetic data. Furthermore, we introduce a client-side node contribution gating mechanism and a server-side sliding window-based clustering strategy to tackle data heterogeneity.
  arXiv  Detail & Related papers  (2026-05-10T09:03:54Z)
• Causally-Constrained Probabilistic Forecasting for Time-Series Anomaly Detection [1.8995711908407733]
  This study introduces a causally-constrained probabilistic forecasting framework for anomaly detection.<n>It integrates an explicit time-lagged causal graph prior with deep sequence modeling.<n>Experiments indicate that the proposed method achieves state-of-the-art detection performance.
  arXiv  Detail & Related papers  (2026-04-20T09:24:28Z)
• Scalable Contrastive Causal Discovery under Unknown Soft Interventions [3.165716101116899]
  We propose a scalable causal discovery model for paired observational and interventional settings with shared underlying causal structure and unknown soft interventions.<n> Experiments on synthetic data demonstrate improved causal structure recovery, generalization to unseen graphs with held-out causal mechanisms, and scalability to larger graphs.
  arXiv  Detail & Related papers  (2026-03-03T18:16:16Z)
• Causal Graph Profiling via Structural Divergence for Robust Anomaly Detection in Cyber-Physical Systems [25.567981742631005]
  Causal Graph-based Anomaly Detection framework designed for reliable cyberattack detection in public infrastructure systems.<n>CGAD follows a two-phase supervised framework -- causal profiling and anomaly scoring.<n>By leveraging causal structures, CGAD achieves superior adaptability and accuracy in non-stationary and imbalanced time series environments.
  arXiv  Detail & Related papers  (2025-08-13T05:26:43Z)
• Local Markov Equivalence and Local Causal Discovery for Identifying Controlled Direct Effects [0.9208007322096533]
  We present a novel algorithm for identifying controlled direct effects (CDEs)<n>Our algorithms require less conditional independence tests and operate under weaker assumptions while maintaining theoretical guarantees.<n>We illustrate the effectiveness of our approach through simulation studies.
  arXiv  Detail & Related papers  (2025-05-05T16:47:29Z)
• Out-of-Distribution Detection on Graphs: A Survey [58.47395497985277]
  Graph out-of-distribution (GOOD) detection focuses on identifying graph data that deviates from the distribution seen during training.<n>We categorize existing methods into four types: enhancement-based, reconstruction-based, information propagation-based, and classification-based approaches.<n>We discuss practical applications and theoretical foundations, highlighting the unique challenges posed by graph data.
  arXiv  Detail & Related papers  (2025-02-12T04:07:12Z)
• Utilizing Causal Network Markers to Identify Tipping Points ahead of Critical Transition [6.7741619010943]
  This paper introduces a framework of causal network markers (CNMs) by incorporating causality indicators.<n>To detect and identify the tipping points ahead of critical transition, two markers are designed.<n>The most possible direction for application includes the identification of tipping points in clinical disease.
  arXiv  Detail & Related papers  (2024-12-19T05:10:47Z)
• Towards Self-Interpretable Graph-Level Anomaly Detection [73.1152604947837]
  Graph-level anomaly detection (GLAD) aims to identify graphs that exhibit notable dissimilarity compared to the majority in a collection. We propose a Self-Interpretable Graph aNomaly dETection model ( SIGNET) that detects anomalous graphs as well as generates informative explanations simultaneously.
  arXiv  Detail & Related papers  (2023-10-25T10:10:07Z)
• Causal Disentanglement Hidden Markov Model for Fault Diagnosis [55.90917958154425]
  We propose a Causal Disentanglement Hidden Markov model (CDHM) to learn the causality in the bearing fault mechanism. Specifically, we make full use of the time-series data and progressively disentangle the vibration signal into fault-relevant and fault-irrelevant factors. To expand the scope of the application, we adopt unsupervised domain adaptation to transfer the learned disentangled representations to other working environments.
  arXiv  Detail & Related papers  (2023-08-06T05:58:45Z)
• 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)
• Block-Approximated Exponential Random Graphs [77.4792558024487]
  An important challenge in the field of exponential random graphs (ERGs) is the fitting of non-trivial ERGs on large graphs. We propose an approximative framework to such non-trivial ERGs that result in dyadic independence (i.e., edge independent) distributions. Our methods are scalable to sparse graphs consisting of millions of nodes.
  arXiv  Detail & Related papers  (2020-02-14T11:42:16Z)

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.