arXiv:2605.27022v1 Announce Type: new 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
The increasing complexity of AI systems and data environments necessitates more sophisticated tools for understanding and resolving issues, making intuitive causal analysis critical.
Causal analysis, when made accessible, empowers domain experts to leverage advanced AI insights, improving decision-making and operational efficiency across critical sectors.
The barrier to entry for complex causal analysis is lowered, allowing non-specialists to interact with and derive actionable insights from sophisticated methodologies through an AI copilot.
- · AI platform developers
- · Domain experts in manufacturing, medicine, social science
- · Enterprises seeking operational efficiency
- · Consultants specializing solely in manual causal analysis
- · Legacy root cause analysis software
Domain experts gain enhanced capabilities for identifying and addressing underlying problems with AI assistance.
Faster and more accurate problem resolution leads to significant economic and social benefits in various industries.
The widespread adoption of AI copilots for complex tasks could accelerate the development of more autonomous problem-solving AI agents.
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Read at arXiv cs.AI