# Large Language Models for Causal Discovery: Current Landscape and Future Directions

arXiv: https://arxiv.org/abs/2402.11068 · HTML: https://mlsanity.com/paper/2402.11068/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 48 references that are in the map.
- First posted on arXiv 16 Feb 2024.

## Builds on (most cited first, 17 of 17)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [Language Models as Knowledge Bases?](https://mlsanity.com/paper/1909.01066/) (2019)
- [DAGs with NO TEARS: Continuous Optimization for Structure Learning](https://mlsanity.com/paper/1803.01422/) (2018)
- [Causal Reasoning and Large Language Models: Opening a New Frontier for Causality](https://mlsanity.com/paper/2305.00050/) (2023)
- [Causal Parrots: Large Language Models May Talk Causality But Are Not Causal](https://mlsanity.com/paper/2308.13067/) (2023)
- [Can Large Language Models Infer Causation from Correlation?](https://mlsanity.com/paper/2306.05836/) (2023)
- [CLadder: Assessing Causal Reasoning in Language Models](https://mlsanity.com/paper/2312.04350/) (2023)
- [Can large language models build causal graphs?](https://mlsanity.com/paper/2303.05279/) (2023)
- [Structure Learning in Graphical Modeling](https://mlsanity.com/paper/1606.02359/) (2016)
- [Causal Discovery with Language Models as Imperfect Experts](https://mlsanity.com/paper/2307.02390/) (2023)
- [Efficient Causal Graph Discovery Using Large Language Models](https://mlsanity.com/paper/2402.01207/) (2024)
- [Causal-Discovery Performance of ChatGPT in the context of Neuropathic Pain Diagnosis](https://mlsanity.com/paper/2301.13819/) (2023)
- [Causal Structure Learning Supervised by Large Language Model](https://mlsanity.com/paper/2311.11689/) (2023)
- [Discovering and Reasoning of Causality in the Hidden World with Large Language Models](https://mlsanity.com/paper/2402.03941/) (2024)
- [Task-Driven Causal Feature Distillation: Towards Trustworthy Risk Prediction](https://mlsanity.com/paper/2312.16113/) (2023)
- [LLM4Causal: Democratized Causal Tools for Everyone via Large Language Model](https://mlsanity.com/paper/2312.17122/) (2023)

## Built on by (most cited first, 25 of 25)

- [Multi-Agent Causal Discovery Using Large Language Models](https://mlsanity.com/paper/2407.15073/) (2024)
- [Sora Detector: A Unified Hallucination Detection for Large Text-to-Video Models](https://mlsanity.com/paper/2405.04180/) (2024)
- [CausalEval: Towards Better Causal Reasoning in Language Models](https://mlsanity.com/paper/2410.16676/) (2024)
- [Disparities in LLM Reasoning Accuracy and Explanations: A Case Study on African American English](https://mlsanity.com/paper/2503.04099/) (2025)
- [Mining Causality: AI-Assisted Search for Instrumental Variables](https://mlsanity.com/paper/2409.14202/) (2024)
- [LLM4GRN: Discovering Causal Gene Regulatory Networks with LLMs -- Evaluation through Synthetic Data Generation](https://mlsanity.com/paper/2410.15828/) (2024)
- [Can Large Language Models Infer Causal Relationships from Real-World Text?](https://mlsanity.com/paper/2505.18931/) (2025)
- [Causal Reasoning in Pieces: Modular In-Context Learning for Causal Discovery](https://mlsanity.com/paper/2507.23488/) (2025)
- [Think Locally, Explain Globally: Graph-Guided LLM Investigations via Local Reasoning and Belief Propagation](https://mlsanity.com/paper/2601.17915/) (2026)
- [Epistemic Regret Minimization: Label-Free Causal Critique Beyond Outcome Reward](https://mlsanity.com/paper/2602.11675/) (2026)
- [Unified Causality Analysis Based on the Degrees of Freedom](https://mlsanity.com/paper/2410.19469/) (2024)
- [Causal Reflection with Language Models](https://mlsanity.com/paper/2508.04495/) (2025)
- [Benchmarking LLMs for Pairwise Causal Discovery in Biomedical and Multi-Domain Contexts](https://mlsanity.com/paper/2601.15479/) (2026)
- [Leveraging Large Language Models for Causal Discovery: a Constraint-based, Argumentation-driven Approach](https://mlsanity.com/paper/2602.16481/) (2026)
- [KG-SoftMAP: Soft Knowledge-Graph Priors for Bayesian Network Structure Learning from Sparse Discrete Data](https://mlsanity.com/paper/2606.10358/) (2026)
- [DMCD: Semantic-Statistical Framework for Causal Discovery](https://mlsanity.com/paper/2602.20333/) (2026)
- [Eliciting associations between clinical variables from LLMs via comparison questions across populations](https://mlsanity.com/paper/2605.06335/) (2026)
- [Evaluating Bivariate Causal Statements Based on Mutual Compatibility](https://mlsanity.com/paper/2606.00278/) (2026)
- [From Prompts to Tokens: Internalizing Causal Supervision in Vision-Language Model for Multi-Image Causal Reasoning](https://mlsanity.com/paper/2606.11745/) (2026)
- [KRCA: An Efficient Root Cause Analysis System in Hyper-scale Microservice Systems via Agentic AI](https://mlsanity.com/paper/2607.01788/) (2026)
- [Integrating Background Knowledge for Scalable Causal Discovery](https://mlsanity.com/paper/2607.10456/) (2026)
- [LLM-Augmented Causal Discovery: Probabilistic Fusion of Edge Existence and Orientation](https://mlsanity.com/paper/2608.27472/) (2026)
- [LEBGen: An LLM-Enhanced Bayesian Network Framework for Few-Shot Travel Survey Data Generation](https://mlsanity.com/paper/2609.08288/) (2026)
- [CausalArena: Benchmarking Causal Discovery in the Foundation Model Era](https://mlsanity.com/paper/2609.11897/) (2026)
- [Contextual Causality with Large Language Models: A Survey](https://mlsanity.com/paper/2609.22409/) (2026)

## Abstract (arXiv)

Causal discovery (CD) and Large Language Models (LLMs) have emerged as transformative fields in artificial intelligence that have evolved largely independently. While CD specializes in uncovering cause-effect relationships from data, and LLMs excel at natural language processing and generation, their integration presents unique opportunities for advancing causal understanding. This survey examines how LLMs are transforming CD across three key dimensions: direct causal extraction from text, integration of domain knowledge into statistical methods, and refinement of causal structures. We systematically analyze approaches that leverage LLMs for CD tasks, highlighting their innovative use of metadata and natural language for causal inference. Our analysis reveals both LLMs' potential to enhance traditional CD methods and their current limitations as imperfect expert systems. We identify key research gaps, outline evaluation frameworks and benchmarks for LLM-based causal discovery, and advocate future research efforts for leveraging LLMs in causality research. As the first comprehensive examination of the synergy between LLMs and CD, this work lays the groundwork for future advances in the field.