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

Publication Date: 7/28/2025

Event: The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Reference: pp. 636–660, 2025

Authors: ChengAo Shen, University of Houston; Zhengzhang Chen , NEC Laboratories America, Inc.; Dongsheng Luo, Florida International University; Dongkuan Xu4, 4North Carolina State University; Haifeng Chen , NEC Laboratories America, Inc.; Jingchao Ni , University of Houston

Abstract: 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 knowledge-driven 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

Publication Link: https://aclanthology.org/2025.findings-acl.36/