ORCA: An End-to-End Interactive Copilot for Optimized Root Cause Analysis

2026-05-26

AI Summary

This work addresses the challenge that causal analysis methods, due to their conceptual complexity and limited validation on real-world data, remain difficult for domain experts to use effectively. To bridge this gap, we propose ORCA—the first end-to-end, interactive causal analysis collaborator designed specifically for non-expert users. ORCA employs a multi-agent architecture to jointly interpret user intent and supports the full causal analysis pipeline, including causal discovery, effect estimation, interpretability analysis, and root cause diagnosis, with adjustable levels of automation ranging from fully automatic to highly manual intervention. The system automatically generates structured reports, visualizations, and performance comparisons, substantially lowering the barrier to entry. Experimental evaluations across multiple real-world scenarios demonstrate that ORCA significantly enhances the efficiency, accuracy, and accessibility of causal analysis.

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.

Problem

• causal analysis
• domain experts
• real-world data
• methodological complexity
• root cause analysis

Innovation

• causal analysis
• root cause analysis
• AI copilot
• causal discovery
• explainability