因果领航员：自主因果分析智能体

Causal-Copilot: An Autonomous Causal Analysis Agent

April 17, 2025

作者: Xinyue Wang, Kun Zhou, Wenyi Wu, Har Simrat Singh, Fang Nan, Songyao Jin, Aryan Philip, Saloni Patnaik, Hou Zhu, Shivam Singh, Parjanya Prashant, Qian Shen, Biwei Huang

cs.AI

摘要

因果分析在科学发现与可靠决策中扮演着基础性角色，然而，由于其概念与算法上的复杂性，领域专家往往难以触及这一工具。因果方法论与实际应用之间的脱节带来了双重挑战：领域专家无法充分利用因果学习的最新进展，而因果研究者则缺乏广泛的现实世界部署来检验和完善其方法。为此，我们推出了Causal-Copilot，一个在大语言模型框架内实现专家级因果分析的自主智能体。Causal-Copilot自动化了针对表格数据和时间序列数据的完整因果分析流程——包括因果发现、因果推断、算法选择、超参数优化、结果解读及可操作见解的生成。它通过自然语言支持交互式精炼，降低了非专业人士的使用门槛，同时保持了方法论的严谨性。通过整合超过20种最先进的因果分析技术，我们的系统促进了良性循环——为领域专家拓宽了高级因果方法的获取途径，同时生成了丰富的现实应用，这些应用不仅指导也推动了因果理论的发展。实证评估表明，Causal-Copilot相较于现有基线展现出卓越性能，提供了一个可靠、可扩展且灵活的解决方案，有效弥合了因果分析中理论精妙与现实应用之间的鸿沟。Causal-Copilot的实时互动演示可在https://causalcopilot.com/访问。

English

Causal analysis plays a foundational role in scientific discovery and reliable decision-making, yet it remains largely inaccessible to domain experts due to its conceptual and algorithmic complexity. This disconnect between causal methodology and practical usability presents a dual challenge: domain experts are unable to leverage recent advances in causal learning, while causal researchers lack broad, real-world deployment to test and refine their methods. To address this, we introduce Causal-Copilot, an autonomous agent that operationalizes expert-level causal analysis within a large language model framework. Causal-Copilot automates the full pipeline of causal analysis for both tabular and time-series data -- including causal discovery, causal inference, algorithm selection, hyperparameter optimization, result interpretation, and generation of actionable insights. It supports interactive refinement through natural language, lowering the barrier for non-specialists while preserving methodological rigor. By integrating over 20 state-of-the-art causal analysis techniques, our system fosters a virtuous cycle -- expanding access to advanced causal methods for domain experts while generating rich, real-world applications that inform and advance causal theory. Empirical evaluations demonstrate that Causal-Copilot achieves superior performance compared to existing baselines, offering a reliable, scalable, and extensible solution that bridges the gap between theoretical sophistication and real-world applicability in causal analysis. A live interactive demo of Causal-Copilot is available at https://causalcopilot.com/.