mlsanityData to 7 Oct 2026

Efficient Causal Graph Discovery Using Large Language Models

arXiv 2402.01207 ↗First posted 2 Feb 2024cs.LG cs.AI stat.MEcited by 51

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Abstract (arXiv)

We propose a novel framework that leverages LLMs for full causal graph discovery. While previous LLM-based methods have used a pairwise query approach, this requires a quadratic number of queries which quickly becomes impractical for larger causal graphs. In contrast, the proposed framework uses a breadth-first search (BFS) approach which allows it to use only a linear number of queries. We also show that the proposed method can easily incorporate observational data when available, to improve performance. In addition to being more time and data-efficient, the proposed framework achieves state-of-the-art results on real-world causal graphs of varying sizes. The results demonstrate the effectiveness and efficiency of the proposed method in discovering causal relationships, showcasing its potential for broad applicability in causal graph discovery tasks across different domains.

Key facts. Cited by 51 papers in the map (data through 7 Oct 2026). Builds on 16 of its 30 references that are in the map. First posted on arXiv 2 Feb 2024.

Builds on

16 of 30 references.

References · 16

  1. 2023GPT-4 Technical Reportcited by 18,132
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  3. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,155
  4. 2023Code Llama: Open Foundation Models for Codecited by 2,105
  5. 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 745
  6. 2023Causal Reasoning and Large Language Models: Opening a New Frontier for Causalitycited by 348
  7. 2023MathPrompter: Mathematical Reasoning using Large Language Modelscited by 236
  8. 2023Large Language Models as Commonsense Knowledge for Large-Scale Task Planningcited by 231
  9. 2023Causal-learn: Causal Discovery in Pythoncited by 108
  10. 2023LLMs and the Abstraction and Reasoning Corpus: Successes, Failures, and the Importance of Object-based Representationscited by 81
  11. 2023Can large language models build causal graphs?cited by 72
  12. 2023Causal Discovery with Language Models as Imperfect Expertscited by 62
4 more
  1. 2022LMPriors: Pre-Trained Language Models as Task-Specific Priorscited by 56
  2. 2022PINTO: Faithful Language Reasoning Using Prompt-Generated Rationalescited by 56
  3. 2023Large Language Models Are Not Strong Abstract Reasonerscited by 52
  4. 2019Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluationcited by 32

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 11; 2025: 17; 2026: 23.

51 papers.

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  1. 2024ALCM: Autonomous LLM-Augmented Causal Discovery Frameworkcited by 29
  2. 2024Large Language Models for Causal Discovery: Current Landscape and Future Directionscited by 25
  3. 2024End-To-End Causal Effect Estimation from Unstructured Natural Language Datacited by 18
  4. 2024Cloud Atlas: Efficient Fault Localization for Cloud Systems using Language Models and Causal Insightcited by 11
  5. 2025Can Large Language Models Help Experimental Design for Causal Discovery?cited by 10
  6. 2024C2P: Featuring Large Language Models with Causal Reasoningcited by 8
  7. 2025A Survey on Enhancing Causal Reasoning Ability of Large Language Modelscited by 8
  8. 2024Counterfactual Causal Inference in Natural Language with Large Language Modelscited by 7
  9. 2024LLM4GRN: Discovering Causal Gene Regulatory Networks with LLMs -- Evaluation through Synthetic Data Generationcited by 7
  10. 2024Prompting Strategies for Enabling Large Language Models to Infer Causation from Correlationcited by 7
  11. 2025CAUSAL3D: A Comprehensive Benchmark for Causal Learning from Visual Datacited by 7
  12. 2025Advancing the Scientific Method with Large Language Models: From Hypothesis to Discoverycited by 7
38 more
  1. 2025LLM Cannot Discover Causality, and Should Be Restricted to Non-Decisional Support in Causal Discoverycited by 7
  2. 2025CausalVLBench: Benchmarking Visual Causal Reasoning in Large Vision-Language Modelscited by 7
  3. 2024Evaluating Interventional Reasoning Capabilities of Large Language Modelscited by 6
  4. 2024LLM-initialized Differentiable Causal Discoverycited by 6
  5. 2025Bayesian Network Structure Discovery Using Large Language Modelscited by 5
  6. 2025Can LLMs Leverage Observational Data? Towards Data-Driven Causal Discovery with LLMscited by 4
  7. 2025Realizing LLMs' Causal Potential Requires Science-Grounded, Novel Benchmarkscited by 4
  8. 2025Revealing Multimodal Causality with Large Language Modelscited by 3
  9. 2026CausalEvolve: Towards Open-Ended Discovery with Causal Scratchpadcited by 3
  10. 2026Causal Scaffolding for Physical Reasoning: A Benchmark for Causally-Informed Physical World Understanding in VLMscited by 3
  11. 2025CauSight: Learning to Supersense for Visual Causal Discoverycited by 2
  12. 2026CausaLab: A Scalable Environment for Interactive Causal Discovery Toward AI Scientistscited by 2
  13. 2026Causal Ensemble Agent: Hierarchical Causal Discovery with LLM-guided Expert Reweightingcited by 2
  14. 2026Autonomous Scientific Discovery via Iterative Meta-Reflectioncited by 2
  15. 2025SOCRATES: Simulation Optimization with Correlated Replicas and Adaptive Trajectory Evaluationscited by 1
  16. 2025CARE: Turning LLMs Into Causal Reasoning Expertcited by 1
  17. 2025CRAwDAD: Causal Reasoning Augmentation with Dual-Agent Debatecited by 1
  18. 2026CASSANDRA: Programmatic and Probabilistic Learning and Inference for Stochastic World Modelingcited by 1
  19. 2026Leveraging Large Language Models for Causal Discovery: a Constraint-based, Argumentation-driven Approachcited by 1
  20. 2026CauTion: Knowing When to Trust LLMs for Ensemble Causal Discoverycited by 1
  21. 2024Retrieving Classes of Causal Orders with Inconsistent Knowledge Basescited by 0
  22. 2025Accelerating Causal Network Discovery of Alzheimer Disease Biomarkers via Scientific Literature-based Retrieval Augmented Generationcited by 0
  23. 2025Causal Cartographer: From Mapping to Reasoning Over Counterfactual Worldscited by 0
  24. 2025Sequential Causal Discovery with Noisy Language Model Priorscited by 0
  25. 2026Causal-Enhanced AI Agents for Medical Research Screeningcited by 0
  26. 2026Step-by-Step Causality: Transparent Causal Discovery with Multi-Agent Tree-Query and Adversarial Confidence Estimationcited by 0
  27. 2026DMCD: Semantic-Statistical Framework for Causal Discoverycited by 0
  28. 2026Beyond the Parameters: A Technical Survey of Contextual Enrichment in Large Language Models: From In-Context Prompting to Causal Retrieval-Augmented Generationcited by 0
  29. 2026CAMO: An Agentic Framework for Automated Causal Discovery from Micro Behaviors to Macro Emergence in LLM Agent Simulationscited by 0
  30. 2026CausalGuard: Conformal Inference under Graph Uncertaintycited by 0
  31. 2026ORCA: An End-to-End Interactive Copilot for Optimized Root Cause Analysiscited by 0
  32. 2026Implicit Causal Graph Construction in Text via Chain Discoverycited by 0
  33. 2026From Prompts to Tokens: Internalizing Causal Supervision in Vision-Language Model for Multi-Image Causal Reasoningcited by 0
  34. 2026CausalSteward: An Agentic Divide-Conquer-Combine Copilot for Causal Discoverycited by 0
  35. 2026Structure Over Scale: Schema-Constrained Causal Graphs for RAGcited by 0
  36. 2026GoT-CD: Graph-of-Thoughts Causal Discovery and the Fragility of Post-hoc Path-Specific Fairness Auditscited by 0
  37. 2026From topology learning to graph generation: A unifying perspectivecited by 0
  38. 2026MaSCoD: A Multi-Agent Framework for Structural-Context-Guided Candidate Causal Graph Generationcited by 0

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