Beyond Correlation: Towards Causal Large Language Model Agents in Biomedicine

• URL: http://arxiv.org/abs/2505.16982v1
• Date: Thu, 22 May 2025 17:52:59 GMT
• Title: Beyond Correlation: Towards Causal Large Language Model Agents in Biomedicine
• Authors: Adib Bazgir, Amir Habibdoust Lafmajani, Yuwen Zhang,
• Abstract summary: Large Language Models (LLMs) show promise in biomedicine but lack true causal understanding, relying instead on correlations.<n>This paper envisions causal LLM agents that integrate multimodal data (text, images, genomics, etc.) and perform intervention-based reasoning to infer cause-and-effect.
• Score: 0.14700417433722487
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Causal Learning Should Embrace the Wisdom of the Crowd [16.587840003381764]
  This paper argues that causal learning is now ready for the emergence of a new paradigm supported by rapidly advancing technologies.<n>We focus on DAG learning for causal discovery and frame the problem as a distributed decision-making task.<n>By proposing a systematic framework to synthesize these insights, we aim to enable the recovery of a global causal structure by any individual agent alone.
  arXiv  Detail & Related papers  (2026-03-03T07:19:24Z)
• Revealing Multimodal Causality with Large Language Models [80.95511545591107]
  We propose MLLM-CD, a novel framework for multimodal causal discovery from unstructured data.<n>It consists of three key components: (1) a novel contrastive factor discovery module to identify genuine multimodal factors; (2) a statistical causal structure discovery module to infer causal relationships among discovered factors; and (3) an iterative multimodal counterfactual reasoning module to refine the discovery outcomes.<n>Extensive experiments on both synthetic and real-world datasets demonstrate the effectiveness of the proposed MLLM-CD.
  arXiv  Detail & Related papers  (2025-09-22T13:45:17Z)
• Causal MAS: A Survey of Large Language Model Architectures for Discovery and Effect Estimation [5.062951330356307]
  Large Language Models (LLMs) have demonstrated remarkable capabilities in various reasoning and generation tasks.<n>Their proficiency in complex causal reasoning, discovery, and estimation remains an area of active development.<n>Multi-agent systems, leveraging the collaborative or specialized abilities of multiple LLM-based agents, are emerging as a powerful paradigm to address these limitations.
  arXiv  Detail & Related papers  (2025-08-31T20:48:31Z)
• Bayesian Hybrid Machine Learning of Gallstone Risk [0.0]
  Gallstone disease is a complex, multifactorial condition with significant global health burdens.<n>We propose a hybrid machine learning framework that integrates robust variable selection with advanced interaction detection.<n>This proposed framework not only enhances prediction but also yields actionable insights, offering a valuable support tool for medical research and decision-making.
  arXiv  Detail & Related papers  (2025-06-17T14:19:02Z)
• Anomaly Detection and Generation with Diffusion Models: A Survey [51.61574868316922]
  Anomaly detection (AD) plays a pivotal role across diverse domains, including cybersecurity, finance, healthcare, and industrial manufacturing.<n>Recent advancements in deep learning, specifically diffusion models (DMs), have sparked significant interest.<n>This survey aims to guide researchers and practitioners in leveraging DMs for innovative AD solutions across diverse applications.
  arXiv  Detail & Related papers  (2025-06-11T03:29:18Z)
• Towards Artificial Intelligence Research Assistant for Expert-Involved Learning [64.7438151207189]
  Large Language Models (LLMs) and Large Multi-Modal Models (LMMs) have emerged as transformative tools in scientific research.<n>We present textbfARtificial textbfIntelligence research assistant for textbfExpert-involved textbfLearning (ARIEL)
  arXiv  Detail & Related papers  (2025-05-03T14:21:48Z)
• Exploring Multi-Modal Integration with Tool-Augmented LLM Agents for Precise Causal Discovery [45.777770849667775]
  Causal inference is an imperative foundation for decision-making across domains, such as smart health, AI for drug discovery and AIOps.<n>We introduce MATMCD, a multi-agent system powered by tool-augmented LLMs.<n>Our empirical study suggests the significant potential of multi-modality enhanced causal discovery.
  arXiv  Detail & Related papers  (2024-12-18T09:50:00Z)
• Causal Representation Learning from Multimodal Biomedical Observations [57.00712157758845]
  We develop flexible identification conditions for multimodal data and principled methods to facilitate the understanding of biomedical datasets.<n>Key theoretical contribution is the structural sparsity of causal connections between modalities.<n>Results on a real-world human phenotype dataset are consistent with established biomedical research.
  arXiv  Detail & Related papers  (2024-11-10T16:40:27Z)
• Combining Domain-Specific Models and LLMs for Automated Disease Phenotyping from Survey Data [0.0]
  This pilot study investigated the potential of combining a domain-specific model, BERN2, with large language models (LLMs) to enhance automated phenotyping from research survey data.<n>We employed BERN2, a named entity recognition and normalization model, to extract information from the ORIGINS survey data.<n>BERN2 demonstrated high performance in extracting and normalizing disease mentions, and the integration of LLMs, particularly with Few Shot Inference and RAG orchestration, further improved accuracy.
  arXiv  Detail & Related papers  (2024-10-28T02:55:03Z)
• Large Language Models in Drug Discovery and Development: From Disease Mechanisms to Clinical Trials [49.19897427783105]
  The integration of Large Language Models (LLMs) into the drug discovery and development field marks a significant paradigm shift. We investigate how these advanced computational models can uncover target-disease linkage, interpret complex biomedical data, enhance drug molecule design, predict drug efficacy and safety profiles, and facilitate clinical trial processes.
  arXiv  Detail & Related papers  (2024-09-06T02:03:38Z)
• Explainable Biomedical Hypothesis Generation via Retrieval Augmented Generation enabled Large Language Models [46.05020842978823]
  Large Language Models (LLMs) have emerged as powerful tools to navigate this complex data landscape. RAGGED is a comprehensive workflow designed to support investigators with knowledge integration and hypothesis generation.
  arXiv  Detail & Related papers  (2024-07-17T07:44:18Z)
• Discovery of the Hidden World with Large Language Models [95.58823685009727]
  This paper presents Causal representatiOn AssistanT (COAT) that introduces large language models (LLMs) to bridge the gap. LLMs are trained on massive observations of the world and have demonstrated great capability in extracting key information from unstructured data. COAT also adopts CDs to find causal relations among the identified variables as well as to provide feedback to LLMs to iteratively refine the proposed factors.
  arXiv  Detail & Related papers  (2024-02-06T12:18:54Z)
• Causal machine learning for single-cell genomics [94.28105176231739]
  We discuss the application of machine learning techniques to single-cell genomics and their challenges. We first present the model that underlies most of current causal approaches to single-cell biology. We then identify open problems in the application of causal approaches to single-cell data.
  arXiv  Detail & Related papers  (2023-10-23T13:35:24Z)
• Emerging Synergies in Causality and Deep Generative Models: A Survey [34.47483716716943]
  Deep generative models (DGMs) have proven adept in capturing complex data distributions but often fall short in generalization and interpretability.<n> causality offers a structured lens to comprehend the mechanisms driving data generation and highlights the causal-effect dynamics inherent in these processes.<n>We elucidate the integration of causal principles within DGMs, investigate causal identification using DGMs, and navigate an emerging research frontier of causality in large-scale generative models.
  arXiv  Detail & Related papers  (2023-01-29T04:10:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.