Exploring Multi-Modal Data with Tool-Augmented LLM Agents for Precise Causal Discovery

作者：ChengAo Shen, Zhengzhang Chen, Dongsheng Luo, Dan Xu, Haifeng Chen, Jingchao Ni · 年份：2025 · DOI：10.18653/v1/2025.findings-acl.36 · 被引用次数：6 · 研究领域：Rough Sets and Fuzzy Logic、Data Quality and Management、Data Mining Algorithms and Applications

Causal discovery is an imperative foundation for decision-making across domains, such as smart health, AI for drug discovery and AIOps.Traditional statistical causal discovery methods, while well-established, predominantly rely on observational data and often overlook the semantic cues inherent in cause-and-effect relationships.The advent of Large Language Models (LLMs) has ushered in an affordable way of leveraging the semantic cues for knowledgedriven causal discovery, but the development of LLMs for causal discovery lags behind other areas, particularly in the exploration of multimodal data.To bridge the gap, we introduce MATMCD, a multi-agent system powered by tool-augmented LLMs.MATMCD has two key agents: a Data Augmentation agent that retrieves and processes modality-augmented data, and a Causal Constraint agent that integrates multi-modal data for knowledge-driven reasoning.The proposed design of the inner-workings ensures successful cooperation of the agents.Our empirical study across seven datasets suggests the significant potential of multi-modality enhanced causal discovery. 1* Corresponding authors. 1 The code of MATMCD is available at https:/