mlsanityData to 14 Oct 2026

Can large language models build causal graphs?

arXiv 2303.05279 ↗First posted 7 Mar 2023cs.CL cs.AIcited by 73

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Abstract (arXiv)

Building causal graphs can be a laborious process. To ensure all relevant causal pathways have been captured, researchers often have to discuss with clinicians and experts while also reviewing extensive relevant medical literature. By encoding common and medical knowledge, large language models (LLMs) represent an opportunity to ease this process by automatically scoring edges (i.e., connections between two variables) in potential graphs. LLMs however have been shown to be brittle to the choice of probing words, context, and prompts that the user employs. In this work, we evaluate if LLMs can be a useful tool in complementing causal graph development.

Key facts. Cited by 73 papers in the map (data through 14 Oct 2026). Builds on 9 of its 24 references that are in the map. First posted on arXiv 7 Mar 2023.

Builds on

9 of 24 references.

References · 9

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2022Large Language Models are Zero-Shot Reasonerscited by 4,374
  3. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,234
  4. 2019BioBERT: a pre-trained biomedical language representation model for biomedical text miningcited by 1,947
  5. 2022Can large language models reason about medical questions?cited by 194
  6. 2021Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyondcited by 178
  7. 2022Thinking about GPT-3 In-Context Learning for Biomedical IE? Think Againcited by 83
  8. 2021GPT-3 Models are Poor Few-Shot Learners in the Biomedical Domaincited by 36
  9. 2022Can Foundation Models Talk Causality?cited by 30

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 14; 2024: 24; 2025: 17; 2026: 18.

73 papers.

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  1. 2023Causal Reasoning and Large Language Models: Opening a New Frontier for Causalitycited by 353
  2. 2023Causal Discovery with Language Models as Imperfect Expertscited by 64
  3. 2023Integrating Large Language Model for Improved Causal Discoverycited by 54
  4. 2024Efficient Causal Graph Discovery Using Large Language Modelscited by 53
  5. 2023Prompt Engineering for Healthcare: Methodologies and Applicationscited by 52
  6. 2023Large Language Models are Temporal and Causal Reasoners for Video Question Answeringcited by 51
  7. 2023Causal Order: The Key to Leveraging Imperfect Experts in Causal Inferencecited by 48
  8. 2025Unveiling Causal Reasoning in Large Language Models: Reality or Mirage?cited by 38
  9. 2024Causal Inference with Large Language Model: A Surveycited by 32
  10. 2024ALCM: Autonomous LLM-Augmented Causal Discovery Frameworkcited by 29
  11. 2024Large Language Models and Causal Inference in Collaboration: A Surveycited by 26
  12. 2024Multi-Agent Causal Discovery Using Large Language Modelscited by 26
38 more
  1. 2024Large Language Models for Causal Discovery: Current Landscape and Future Directionscited by 25
  2. 2024Large Language Models are Effective Priors for Causal Graph Discoverycited by 24
  3. 2024Testing and Understanding Erroneous Planning in LLM Agents through Synthesized User Inputscited by 22
  4. 2023Causal Structure Learning Supervised by Large Language Modelcited by 21
  5. 2024Causal Evaluation of Language Modelscited by 18
  6. 2024End-To-End Causal Effect Estimation from Unstructured Natural Language Datacited by 18
  7. 2023From Text to Structure: Using Large Language Models to Support the Development of Legal Expert Systemscited by 17
  8. 2025Causally Reliable Concept Bottleneck Modelscited by 17
  9. 2024Causal Graph Discovery with Retrieval-Augmented Generation based Large Language Modelscited by 16
  10. 2025Auto-Discovery-Bench: Diagnosing Structured State Tracking in Oracle-Guided Discoverycited by 16
  11. 2024Large Language Models for Constrained-Based Causal Discoverycited by 15
  12. 2023GPT-Based Models Meet Simulation: How to Efficiently Use Large-Scale Pre-Trained Language Models Across Simulation Taskscited by 14
  13. 2024RealTCD: Temporal Causal Discovery from Interventional Data with Large Language Modelcited by 13
  14. 2024On the Reliability of Large Language Models for Causal Discoverycited by 12
  15. 2024CausalGraph2LLM: Evaluating LLMs for Causal Queriescited by 12
  16. 2025Can Large Language Models Help Experimental Design for Causal Discovery?cited by 11
  17. 2023LLM4Causal: Democratized Causal Tools for Everyone via Large Language Modelcited by 9
  18. 2025LLM Cannot Discover Causality, and Should Be Restricted to Non-Decisional Support in Causal Discoverycited by 8
  19. 2024Mining Causality: AI-Assisted Search for Instrumental Variablescited by 7
  20. 2024Prompting Strategies for Enabling Large Language Models to Infer Causation from Correlationcited by 7
  21. 2023Mitigating Prior Errors in Causal Structure Learning: A Resilient Approach via Bayesian Networkscited by 5
  22. 2023Causal discovery using dynamically requested knowledgecited by 5
  23. 2023An Explainable AI Approach to Large Language Model Assisted Causal Model Auditing and Developmentcited by 5
  24. 2024Simulating Policy Impacts: Developing a Generative Scenario Writing Method to Evaluate the Perceived Effects of Regulationcited by 5
  25. 2025Fairness-Driven LLM-based Causal Discovery with Active Learning and Dynamic Scoringcited by 5
  26. 2025Bayesian Network Structure Discovery Using Large Language Modelscited by 5
  27. 2023Towards CausalGPT: A Multi-Agent Approach for Faithful Knowledge Reasoning via Promoting Causal Consistency in LLMscited by 4
  28. 2025Realizing LLMs' Causal Potential Requires Science-Grounded, Novel Benchmarkscited by 4
  29. 2025How to Auto-optimize Prompts for Domain Tasks? Adaptive Prompting and Reasoning through Evolutionary Domain Knowledge Adaptationcited by 4
  30. 2025GraphLocator: Graph-guided Causal Reasoning for Issue Localizationcited by 4
  31. 2026CausaLab: A Scalable Environment for Interactive Causal Discovery Toward AI Scientistscited by 4
  32. 2024Foundation Model Sherpas: Guiding Foundation Models through Knowledge and Reasoningcited by 3
  33. 2024Scalability of Bayesian Network Structure Elicitation with Large Language Models: a Novel Methodology and Comparative Analysiscited by 3
  34. 2024CausalChat: Interactive Causal Model Development and Refinement Using Large Language Modelscited by 3
  35. 2025Uncovering Bias Paths with LLM-guided Causal Discovery: An Active Learning and Dynamic Scoring Approachcited by 3
  36. 2025Causal Graph based Event Reasoning using Semantic Relation Expertscited by 2
  37. 2025Causal MAS: A Survey of Large Language Model Architectures for Discovery and Effect Estimationcited by 2
  38. 2025IRIS: An Iterative and Integrated Framework for Verifiable Causal Discovery in the Absence of Tabular Datacited by 2

50 most cited of 73 All 73 (JSON)