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StableRCA is a novel root cause analysis framework that identifies intervention targets by estimating local Markov boundaries and detecting conditional distribution shifts, avoiding the need for global causal graph discovery and demonstrating robustness across synthetic and real-world datasets.
This paper introduces a formal definition of causal pathways for rare events and discusses testable implications, bridging simple verbal explanations with detailed causal models.
ORCA is a copilot for end-to-end causal analysis that uses agents to guide users through workflows including causal discovery, effect estimation, and root cause analysis, with structured reports.
TopoEvo is a topology-aware self-evolving multi-agent framework for root cause analysis in microservices that couples graph representation learning with structured, topology-constrained reasoning. It achieves absolute improvements of up to 3.44% in root cause localization accuracy and boosts fault-type classification performance by 4.39% to 16.81% across diverse datasets.
STAR is a stage-attributed triage and repair framework that decomposes LLM-based RCA agent workflows into four structured stages, enabling stage-wise auditing, counterfactual evaluation, and patch-and-replay repair to improve root cause localization and fault type classification in microservice AIOps.
This guide from OpenAI Academy explains how data science teams can use Codex to speed up analysis workflows, including root-cause analysis, business impact readouts, and handling ambiguous requests.