CauScientist: Teaching LLMs to Respect Data for Causal Discovery

• URL: http://arxiv.org/abs/2601.13614v1
• Date: Tue, 20 Jan 2026 05:32:22 GMT
• Title: CauScientist: Teaching LLMs to Respect Data for Causal Discovery
• Abstract summary: CauScientist is a framework that synergizes "data scientists" with probabilistic statistics as rigorous "verifiers"<n>Experiments demonstrate that CauScientist substantially outperforms purely data-driven baselines.<n>CauScientist reduces structural hamming distance (SHD) by 44.0% compared to Qwen3-32B on 37-node graphs.
• Score: 25.77810792733626
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Causal discovery is fundamental to scientific understanding and reliable decision-making. Existing approaches face critical limitations: purely data-driven methods suffer from statistical indistinguishability and modeling assumptions, while recent LLM-based methods either ignore statistical evidence or incorporate unverified priors that can mislead result. To this end, we propose CauScientist, a collaborative framework that synergizes LLMs as hypothesis-generating "data scientists" with probabilistic statistics as rigorous "verifiers". CauScientist employs hybrid initialization to select superior starting graphs, iteratively refines structures through LLM-proposed modifications validated by statistical criteria, and maintains error memory to guide efficient search space. Experiments demonstrate that CauScientist substantially outperforms purely data-driven baselines, achieving up to 53.8% F1 score improvement and enhancing recall from 35.0% to 100.0%. Notably, while standalone LLM performance degrades with graph complexity, CauScientist reduces structural hamming distance (SHD) by 44.0% compared to Qwen3-32B on 37-node graphs. Our project page is at https://github.com/OpenCausaLab/CauScientist.

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