title: Causal-Copilot: An Autonomous Causal Analysis Agent
summary: 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/.
paper_authors._id: 6808977e51649749b20850f0
paper_authors.name: Xinyue Wang
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paper_authors.name: Kun Zhou
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paper_authors.fullname: Kun Zhou
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paper_authors.user: FrancisKunZhou
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paper_authors.name: Wenyi Wu
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paper_authors.fullname: Wenyi Wu
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paper_authors.user: WenyiWU0111
paper_authors._id: 6808977e51649749b20850f3
paper_authors.name: Har Simrat Singh
paper_authors._id: 6808977e51649749b20850f4
paper_authors.name: Fang Nan
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paper_authors.fullname: Fang Nan
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paper_authors.user: fangn06
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paper_authors.name: Songyao Jin
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paper_authors.name: Aryan Philip
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paper_authors.fullname: Aryan Philip
paper_authors.type: user
paper_authors.user: arphilip123
paper_authors._id: 6808977e51649749b20850f7
paper_authors.name: Saloni Patnaik
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-04-24T20:13:27.175Z
paper_authors._id: 669bc5e4119595d21bf06299
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paper_authors.fullname: Saloni Patnaik
paper_authors.type: user
paper_authors.user: sapatnaik
paper_authors._id: 6808977e51649749b20850f8
paper_authors.name: Hou Zhu
paper_authors._id: 6808977e51649749b20850f9
paper_authors.name: Shivam Singh
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paper_authors.name: Parjanya Prashant
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paper_authors.fullname: PARJANYA PRAJAKTA PRASHANT
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paper_authors.user: parjanya20
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paper_authors.name: Qian Shen
paper_authors._id: 6808977e51649749b20850fc
paper_authors.name: Biwei Huang
paper_title: Causal-Copilot: An Autonomous Causal Analysis Agent
paper_summary: 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/.
paper_ai_summary: Causal-Copilot automates expert-level causal analysis for both tabular and time-series data, integrating over 20 state-of-the-art techniques to bridge the gap between causal theory and practical usability.
paper_ai_keywords: causal analysis causal discovery causal inference algorithm selection hyperparameter optimization result interpretation actionable insights natural language time-series data Causal-Copilot