arXiv AI
Overcoming Challenges of Interpretive Structural Modeling with Large Language Models
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MaSCoD: A Multi-Agent Framework for Structural-Context-Guided Candidate Causal Graph Generation
MaSCoD is a multi‑agent framework that explicitly addresses the premature omission of causal relations by first organizing candidate third variables and local structural patterns before making direct‑edge judgments. Using GPT‑5.4 and GPT‑4o as backbones, the study evaluates MaSCoD on Auto‑MPG, DWD, and Sachs datasets, finding that the Full phase—where structural hypotheses are supplied early—generally improves recall and F1 scores compared to a No Phase 1 approach, though it can also raise false‑positive rates. Ablation experiments reveal that providing both structural and contextual information does not always outperform providing only one, and that the benefits of the Full phase vary across datasets and backbones. whyItMatters":"The paper demonstrates that explicitly pre‑organizing structural context can improve causal graph generation performance, highlighting the importance of designing omission control mechanisms in causal discovery with large language models."
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