Causal-Copilot: An Autonomous Causal Analysis Agent

• URL: http://arxiv.org/abs/2504.13263v2
• Date: Mon, 21 Apr 2025 17:58:08 GMT
• Title: Causal-Copilot: An Autonomous Causal Analysis Agent
• Authors: 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,
• Abstract summary: Causal-Copilot is an autonomous agent that operationalizes expert-level causal analysis.<n>It supports interactive refinement through natural language, lowering the barrier for non-specialists.<n>Our system fosters a virtuous cycle -- expanding access to advanced causal methods for domain experts.
• Score: 25.5944359329613
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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/.

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