Causal-Copilot: An Autonomous Causal Analysis Agent
Authors: name: Xinyue Wang, name: Kun Zhou, name: Wenyi Wu, name: Har Simrat Singh, name: Fang Nan, name: Songyao Jin, name: Aryan Philip, name: Saloni Patnaik, name: Hou Zhu, name: Shivam Singh, name: Parjanya Prashant, name: Qian Shen, name: Biwei Huang

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/.