@inproceedings{shen-etal-2025-exploring,
    title = "Exploring Multi-Modal Data with Tool-Augmented {LLM} Agents for Precise Causal Discovery",
    author = "Shen, ChengAo  and
      Chen, Zhengzhang  and
      Luo, Dongsheng  and
      Xu, Dongkuan  and
      Chen, Haifeng  and
      Ni, Jingchao",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/transition-to-people-yaml/2025.findings-acl.36/",
    doi = "10.18653/v1/2025.findings-acl.36",
    pages = "636--660",
    ISBN = "979-8-89176-256-5",
    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 multi-modal 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."
}