Beyond Correlation: Towards Causal Large Language Model Agents in Biomedicine

TL;DR

This paper advocates for developing causal large language model agents in biomedicine that can perform intervention-based reasoning, integrate multimodal data, and facilitate transformative applications like drug discovery and personalized medicine.

Contribution

It proposes a comprehensive research agenda for creating causal LLM agents in biomedicine, emphasizing multimodal integration, causal reasoning, and evaluation benchmarks.

Findings

Identifies key challenges in designing causal LLM agents.

Highlights potential applications in drug discovery and personalized medicine.

Suggests integrating structured knowledge and causal inference tools.

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.