mlsanityData to 7 Oct 2026

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

arXiv 2412.13667 ↗First posted 18 Dec 2024cs.LG cs.AI stat.MEcited by 11

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

Abstract (arXiv)

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. MATMCD has two key agents: a Data Augmentation agent that retrieves and processes modality-augmented data, and a Causal Constraint agent that integrates multi-modal data for knowledge-driven reasoning. The proposed design of the inner-workings ensures successful cooperation of the agents. Our empirical study across seven datasets suggests the significant potential of multi-modality enhanced causal discovery.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use. Builds on 23 of its 51 references that are in the map. First posted on arXiv 18 Dec 2024.

Builds on

23 of 51 references.

References · 23

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2023GPT-4 Technical Reportcited by 18,132
  3. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  4. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  5. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  6. 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
  7. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
  8. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,317
  9. 2023CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Societycited by 1,348
  10. 2023Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debatecited by 995
  11. 2023Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMscited by 946
  12. 2023Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedbackcited by 788
11 more
  1. 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 745
  2. 2023Causal Reasoning and Large Language Models: Opening a New Frontier for Causalitycited by 348
  3. 2022MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoningcited by 153
  4. 2023Causal Parrots: Large Language Models May Talk Causality But Are Not Causalcited by 146
  5. 2022Score matching enables causal discovery of nonlinear additive noise modelscited by 108
  6. 2023Causal-learn: Causal Discovery in Pythoncited by 108
  7. 2023Examining Inter-Consistency of Large Language Models Collaboration: An In-depth Analysis via Debatecited by 94
  8. 2023Causal Discovery with Language Models as Imperfect Expertscited by 62
  9. 2019Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluationcited by 32
  10. 2024Integrating Large Language Models in Causal Discovery: A Statistical Causal Approachcited by 31
  11. 2023Causal Inference in Gene Regulatory Networks with GFlowNet: Towards Scalability in Large Systemscited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 9.

11 papers.

  1. 2026From Multi-Agent to Single-Agent: When Is Skill Distillation Beneficial?cited by 6
  2. 2025Benchmarking Chinese Medical LLMs: A Medbench-based Analysis of Performance Gaps and Hierarchical Optimization Strategiescited by 2
  3. 2026Closing the Auto-Research Loop: An AI Co-Scientist for Production Search Rankingcited by 1
  4. 2026ClimateCause: Complex and Implicit Causal Structures in Climate Reportscited by 1
  5. 2026STAR: Failure-Aware Markovian Routing for Multi-Agent Spatiotemporal Reasoningcited by 1
  6. 2026Consistency evaluation of benchmarks used for causal discoverycited by 1
  7. 2025Beyond Correlation: Towards Causal Large Language Model Agents in Biomedicinecited by 0
  8. 2026DMCD: Semantic-Statistical Framework for Causal Discoverycited by 0
  9. 2026InferenceEvolve: Towards Automated Causal Effect Estimators through Self-Evolving AIcited by 0
  10. 2026CAMO: An Agentic Framework for Automated Causal Discovery from Micro Behaviors to Macro Emergence in LLM Agent Simulationscited by 0
  11. 2026CausalSmith: A Formally Grounded, Self-Improving Agentic Framework for Automated Research in Causal Inferencecited by 0

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. 2024Multi-Agent Causal Discovery Using Large Language Models
  3. 2024ALCM: Autonomous LLM-Augmented Causal Discovery Framework
  4. 2024LLM-initialized Differentiable Causal Discovery
  5. 2026Traceable Latent Variable Discovery Based on Multi-Agent Collaboration
  6. 2025CARE: Turning LLMs Into Causal Reasoning Expert
  7. 2025Revealing Multimodal Causality with Large Language Models
  8. 2025Can LLMs Leverage Observational Data? Towards Data-Driven Causal Discovery with LLMs