Causal Discovery in the Era of Agents
Summary
This paper argues that language model agents should assist causal discovery workflows by providing contextual support and explanations rather than generating causal conclusions, and introduces causal-learn+ platform to demonstrate this principle.
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Paper page - Causal Discovery in the Era of Agents
Source: https://huggingface.co/papers/2606.23608
Abstract
Language models should assist causal discovery workflows by providing contextual support and explanations rather than generating causal conclusions, as demonstrated through a platform that integrates data analysis and expert knowledge.
Recent attempts to combinelarge language models(LLMs) withcausal discoveryask models to infer pairwise directions, propose graph structures, or inject language-model outputs as priors and constraints. These approaches promise faster analysis, but they also obscure whether a causal evidence is supported by data and assumptions or by textual associations, prompt artifacts and hallucinated mechanisms. We argue for a different role for agents incausal discovery. Agents should inspect data, retrieve context, explain method assumptions and clarify graph outputs, but they should not supply edges, orientations, priors, constraints or causal conclusions. We propose the principle that agents assist the workflow, whilecausal claimsremain grounded in data, explicit assumptions,formal algorithms, diagnostics and user or domain-expert decisions. We instantiate this principle incausal-learn+, an online platform that coordinatesdata analysis, preprocessing, method recommendation,expert-knowledge incorporation, formal discovery and interpretation around the algorithmic ecosystem ofcausal-learn. A case study on Big Five personality data illustrates agent-assisted pipeline ofcausal discoverywithout turning language-model unreliability into causal evidence. The platform is available at causallearn.com.
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