aiqbench analysis
This research introduces a tag-based framework for Directed Acyclic Graphs (DAGs) that mitigates the unreliability of Large Language Models in causal discovery by learning higher-level relations directly from identifiable data structures. By leveraging semantically meaningful variable tags, the method successfully orients undirected edges to resolve causal structures beyond the Markov Equivalence Class without depending on fallible external knowledge. The approach represents a significant advancement in hybrid strategies that balance semantic guidance with rigorous data-driven validation for specialized domains.
Tags for DAGs: Graph Refinement with Meta-Informed Relations
arXiv:2506.19459v2 Announce Type: replace Abstract: Causal discovery has shifted from data-centric methods to hybrid strategies that integrate semantic knowledge from experts or large language models (LLMs). Such external information is vital for identifying causal structures beyond the Markov Equivalence Class (MEC), which data alone cannot resolve. However, expert availability is often limited, and LLMs frequently misidentify causal directions in specialized domains. To overcome such shortcomings, we propose a tag-based approach that leverages semantically meaningful labels while deriving causal directionality directly from data. Using variable-level tag assignments from available sources (e.g., LLMs), our tags for DAGs method learns from identifiable data structures to extract higher-level causal relations. These are then used to orient undirected edges, enabling causal discovery to move beyond the MEC without reliance on fallible external knowledge.
Abstract:Causal discovery has shifted from data-centric methods to hybrid strategies that integrate semantic knowledge from experts or large language models (LLMs). Such external information is vital for identifying causal structures beyond the Markov Equivalence Class (MEC), which data alone cannot resolve. However, expert availability is often limited, and LLMs frequently misidentify causal directions in specialized domains. To overcome such shortcomings, we propose a tag-based approach that leverages semantically meaningful labels while deriving causal directionality directly from data. Using variable-level tag assignments from available sources (e.g., LLMs), our tags for DAGs method learns from identifiable data structures to extract higher-level causal relations. These are then used to orient undirected edges, enabling causal discovery to move beyond the MEC without reliance on fallible external knowledge.
Accepted to Transactions on Machine Learning Research (TMLR). Camera-ready version
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2506.19459 [cs.LG]
(or arXiv:2506.19459v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2506.19459 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 228 words
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