MaSCoD: A Multi-Agent Framework for Structural-Context-Guided Candidate Causal Graph Generation

arXiv:2609.19944 · cs.AI · Submitted 2026-09-17 · Read on arXiv

cs.AI

Submitted: 2026-09-17

Updated: 2026-09-17

Comments: 31 pages, 4 figures, 18 tables. The first two authors contributed equally

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Ablation — 19× in this paper · explained in 1 episode
• Causal Discovery — 16× in this paper · explained in 2 episodes
• Consistency — 9× in this paper · explained in 3 episodes
• Aggregation — 6× in this paper · explained in 1 episode
• Multi-Agent Framework — 5× in this paper · explained in 3 episodes
• Abundance — 4× in this paper · explained in 1 episode
• Benchmark — 4× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 3× in this paper · explained in 6 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Causal Inference — 2× in this paper · explained in 4 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Hamming Distance — 2× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Feature Engineering — 1× in this paper · explained in 3 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Physical Constraints — 1× in this paper · explained in 7 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Selection Bias — 1× in this paper · explained in 4 episodes
• Structural Reasoning — 1× in this paper · explained in 2 episodes

Sources

• Efficient Causal Graph Discovery Using Large Language Models
• Multi-Agent Causal Discovery Using Large Language Models

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