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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.13263 |
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| _version_ | 1866915251315802112 |
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| author | Wang, Xinyue Zhou, Kun Wu, Wenyi Singh, Har Simrat Nan, Fang Jin, Songyao Philip, Aryan Patnaik, Saloni Zhu, Hou Singh, Shivam Prashant, Parjanya Shen, Qian Huang, Biwei |
| author_facet | Wang, Xinyue Zhou, Kun Wu, Wenyi Singh, Har Simrat Nan, Fang Jin, Songyao Philip, Aryan Patnaik, Saloni Zhu, Hou Singh, Shivam Prashant, Parjanya Shen, Qian Huang, Biwei |
| contents | 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/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_13263 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Causal-Copilot: An Autonomous Causal Analysis Agent Wang, Xinyue Zhou, Kun Wu, Wenyi Singh, Har Simrat Nan, Fang Jin, Songyao Philip, Aryan Patnaik, Saloni Zhu, Hou Singh, Shivam Prashant, Parjanya Shen, Qian Huang, Biwei Artificial Intelligence 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/. |
| title | Causal-Copilot: An Autonomous Causal Analysis Agent |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2504.13263 |