CausalSteward: An Agentic Divide-Conquer-Combine Copilot for Causal Discovery
Learning causal models from high-dimensional data is a significant challenge, particularly in real-world settings where violations of core assumptions lead to causal identifiability issues. Although massive amounts of prior knowledge are available, and contain valuable causal information, effectively integrating this knowledge into the causal discovery process remains an open problem. We introduce CausalSTeward (CAST), a novel human-in-the-loop framework for interactively assembling large causal models. CausalSteward is a multi-agent collaborative system that tackles high-dimensional causality through a divide-and-conquer approach where large clusters of variables are iteratively partitio...
| Authors | Nicholas Tagliapietra, Gian Lorenzo Marchioni, Moritz Willig et al. |
|---|---|
| Primary category | cs.MA |
| Published | 2 July 2026 |
| Last updated at source | 2 July 2026 |
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