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

Understand

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.