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 timeseries 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.