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.

翻译：因果推断是智能健康、AI药物发现及AIOps等多个领域决策制定的关键基础。传统统计因果发现方法虽已成熟，但主要依赖观测数据，常忽略因果关系中固有的语义线索。大型语言模型（LLMs）的出现为利用语义线索进行知识驱动的因果发现提供了可行路径，但面向因果发现的LLM发展仍滞后于其他领域，尤其在多模态数据探索方面。为弥补这一差距，本文提出MATMCD——一个由工具增强型LLM驱动的多智能体系统。MATMCD包含两个核心智能体：用于检索和处理模态增强数据的**数据增强智能体**，以及整合多模态数据进行知识驱动推理的**因果约束智能体**。通过精心设计的内部协作机制确保智能体间的有效配合。我们在七个数据集上的实证研究表明，多模态增强的因果发现具有显著潜力。