mlsanityData to 14 Oct 2026

Beyond Correlation: Towards Causal Large Language Model Agents in Biomedicine

arXiv 2505.16982 ↗First posted 22 May 2025cs.AI physics.med-phcited by 0

On the map: LLM agents and tool use → Medical and scientific domain agents

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use. Builds on 9 of its 22 references that are in the map. First posted on arXiv 22 May 2025.

Builds on

9 of 22 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024Exploring Multi-Modal Data with Tool-Augmented LLM Agents for Precise Causal Discovery18 Dec
  2. 2024Causal Agent based on Large Language Model13 Aug

Other references · 7

  1. 2023Causal Reasoning and Large Language Models: Opening a New Frontier for Causalitycited by 353
  2. 2024Empowering Biomedical Discovery with AI Agentscited by 118
  3. 2024Causality for Large Language Modelscited by 25
  4. 2024ChatDiet: Empowering Personalized Nutrition-Oriented Food Recommender Chatbots through an LLM-Augmented Frameworkcited by 20
  5. 2023TRIALSCOPE: A Unifying Causal Framework for Scaling Real-World Evidence Generation with Biomedical Language Modelscited by 7
  6. 2024MedG-KRP: Medical Graph Knowledge Representation Probingcited by 2
  7. 2025A Large-Scale Vision-Language Dataset Derived from Open Scientific Literature to Advance Biomedical Generalist AIcited by 2

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2025Causal MAS: A Survey of Large Language Model Architectures for Discovery and Effect Estimation
  2. 2025LLM-based Agents for Automated Confounder Discovery and Subgroup Analysis in Causal Inference
  3. 2024A Survey for Large Language Models in Biomedicine
  4. 2026Benchmarking LLMs for Pairwise Causal Discovery in Biomedical and Multi-Domain Contexts
  5. 2024Large Language Models and Causal Inference in Collaboration: A Survey
  6. 2025CARE: Turning LLMs Into Causal Reasoning Expert
  7. 2025Scientific Hypothesis Generation and Validation: Methods, Datasets, and Future Directions
  8. 2026Leveraging Large Language Models for Causal Discovery: a Constraint-based, Argumentation-driven Approach