Causal-Copilot: An Autonomous Causal Analysis Agent
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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Xinyue Wang
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Kun Zhou
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Kun Zhou
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Wenyi Wu
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Har Simrat Singh
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Fang Nan
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Fang Nan
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Songyao Jin
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Aryan Philip
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Aryan Philip
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Saloni Patnaik
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Saloni Patnaik
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Parjanya Prashant
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PARJANYA PRAJAKTA PRASHANT
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parjanya20
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Qian Shen
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Biwei Huang
Causal-Copilot: An Autonomous Causal Analysis Agent
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/.
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.
causal analysis causal discovery causal inference algorithm selection hyperparameter optimization result interpretation actionable insights natural language time-series data Causal-Copilot