因果副駕駛：自主因果分析代理

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

因果分析在科學發現與可靠決策中扮演著基礎性角色，然而由於其概念與算法上的複雜性，這一方法對領域專家而言仍難以觸及。因果方法論與實際應用之間的這種脫節帶來了雙重挑戰：領域專家無法利用因果學習的最新進展，而因果研究者則缺乏廣泛的現實世界部署來測試和完善其方法。為解決這一問題，我們推出了Causal-Copilot，這是一款在大型語言模型框架內實現專家級因果分析的自動化代理。Causal-Copilot自動化完成了針對表格數據和時間序列數據的完整因果分析流程——包括因果發現、因果推斷、算法選擇、超參數優化、結果解釋以及可操作見解的生成。它支持通過自然語言進行交互式精煉，降低了非專業人士的使用門檻，同時保持了方法論的嚴謹性。通過整合超過20種最先進的因果分析技術，我們的系統促進了良性循環——為領域專家擴展了先進因果方法的可及性，同時生成了豐富的現實世界應用，這些應用反過來又啟發並推動了因果理論的發展。實證評估表明，Causal-Copilot相較於現有基線展現出更優異的性能，提供了一個可靠、可擴展且可延伸的解決方案，有效彌合了因果分析中理論精妙與現實應用之間的鴻溝。Causal-Copilot的實時互動演示可在https://causalcopilot.com/上訪問。

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/.