ORCA: An End-to-End Interactive Copilot for Optimized Root Cause Analysis
The article introduces ORCA, an interactive copilot designed for optimized root cause analysis. It aims to make causal analysis more accessible to domain experts by guiding them through various workflows. ORCA includes features such as causal discovery, effect estimation, and structured reporting, demonstrating effectiveness in real-world applications.
- ▪Causal analysis is important in fields like manufacturing, social science, and medicine.
- ▪ORCA helps users navigate causal analysis workflows, from automatic to user-guided processes.
- ▪The tool evaluates performance, generates metrics, and produces insights through reports.
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Computer Science > Artificial Intelligence arXiv:2605.27022 (cs) [Submitted on 26 May 2026] Title:ORCA: An End-to-End Interactive Copilot for Optimized Root Cause Analysis Authors:Phi Nguyen Xuan, Nicholas Tagliapietra, Lavdim Halilaj, Kristian Kersting, Juergen Luettin View a PDF of the paper titled ORCA: An End-to-End Interactive Copilot for Optimized Root Cause Analysis, by Phi Nguyen Xuan and 4 other authors View PDF HTML (experimental) Abstract:Causal analysis is a crucial task in many domains, including manufacturing, social science, and medicine. However, despite recent progress, the conceptual and methodological complexity of causal methods makes them largely inaccessible to domain experts.
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