Publication
ORCA: An End-to-End Interactive Copilot for Optimized Root Cause Analysis
Phi Nguyen Xuan; Nicholas Tagliapietra; Lavdim Halilaj; Kristian Kersting; Juergen Luettin
In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2605.27022, Pages 1-10, arXiv, 2026.
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. This gap prevents experts from leveraging these advances and hinders researchers
who lack access to real-world data for validation. To bridge this divide, we introduce ORCA, a copilot for
end-to-end causal analysis. ORCA orchestrates agents to understand the user’s goals and guide them through the
most appropriate causal analysis workflow, from fully automatic to highly user-guided execution. It features
causal discovery, causal effect estimation, explainability and Root-Cause-Analysis (RCA). ORCA evaluates and
compares performance, generates key metrics and diagrams, and generates insights through structured reports.
We highlight its effectiveness across several real-world use-cases.