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

Causal inference 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-modality 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 inference. Delicate 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.

@article{shen2025_2412.13667,
  title={ Exploring Multi-Modal Data with Tool-Augmented LLM Agents for Precise Causal Discovery },
  author={ ChengAo Shen and Zhengzhang Chen and Dongsheng Luo and Dongkuan Xu and Haifeng Chen and Jingchao Ni },
  journal={arXiv preprint arXiv:2412.13667},
  year={ 2025 }
}