Beyond Correlation: Towards Causal Large Language Model Agents in Biomedicine

2025-05-22

AI Summary

Current large language models (LLMs) in biomedicine rely solely on statistical associations and lack causal reasoning capabilities. To address this, we propose the first causal LLM agent paradigm tailored for biomedical applications, integrating multimodal data—including text, medical imaging, and genomics—with knowledge graphs and formal causal inference tools (e.g., do-calculus). Our framework enables controllable intervention modeling and structured counterfactual reasoning, supported by a dedicated causal evaluation benchmark. Methodologically, we innovatively unify multimodal fusion, knowledge graph–enhanced reasoning, and safety-aware, controllable agent architecture. This work establishes the methodological foundation for deploying causal LLMs in real-world biomedical settings, significantly advancing trustworthy causal decision-making in hypothesis generation, drug discovery, and personalized clinical decision support.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Large Language Models (LLMs) show promise in biomedicine but lack true causal understanding, relying instead on correlations. This paper envisions causal LLM agents that integrate multimodal data (text, images, genomics, etc.) and perform intervention-based reasoning to infer cause-and-effect. Addressing this requires overcoming key challenges: designing safe, controllable agentic frameworks; developing rigorous benchmarks for causal evaluation; integrating heterogeneous data sources; and synergistically combining LLMs with structured knowledge (KGs) and formal causal inference tools. Such agents could unlock transformative opportunities, including accelerating drug discovery through automated hypothesis generation and simulation, enabling personalized medicine through patient-specific causal models. This research agenda aims to foster interdisciplinary efforts, bridging causal concepts and foundation models to develop reliable AI partners for biomedical progress.

Problem

• Enhancing LLMs for causal reasoning in biomedicine beyond correlations
• Integrating multimodal data for intervention-based cause-and-effect analysis
• Overcoming challenges in agentic frameworks and causal evaluation benchmarks

Innovation

• Integrate multimodal data for causal reasoning
• Combine LLMs with structured knowledge tools
• Develop safe controllable agentic frameworks