ORCA让非专家也能轻松做因果分析，自动完成从数据到根因的全流程。
ORCA: An End-to-End Interactive Copilot for Optimized Root Cause Analysis
- 用多智能体协作理解用户目标，自动规划因果分析流程。
- 支持从全自动到人工干预的灵活模式，可生成指标、图表和结构化报告。
- 适合医疗、制造等领域的研究人员和业务专家快速定位问题根源。
因果分析在制造、社会科学和医学等领域至关重要。然而，当前因果方法概念复杂、使用门槛高，使领域专家难以应用，也限制了缺乏真实数据的研究者进行验证。为弥合这一差距，我们提出ORCA——一个端到端的交互式因果分析协作者。ORCA通过协调多个智能体，理解用户目标，并引导其完成从完全自动化到高度用户主导的因果分析流程，涵盖因果发现、因果效应估计、可解释性与根因分析（RCA）。系统可评估并比较性能，生成关键指标与图表，并通过结构化报告输出洞察。我们在多个真实场景中验证了其有效性。
原文摘要 · Abstract (English)
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.
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