mlsanityData to 7 Oct 2026

C2P: Featuring Large Language Models with Causal Reasoning

arXiv 2407.18069 ↗First posted 25 Jul 2024cs.LOcited by 8

On the map: LLM reasoning → Causal reasoning in LLMs

Abstract (arXiv)

Causal reasoning is one of the primary bottlenecks that Large Language Models (LLMs) must overcome to attain human-level intelligence. Recent studies indicate that LLMs display near-random performance on reasoning tasks. To address this, we introduce the Causal Chain of Prompting (C2P), a reasoning framework that aims to equip current LLMs with causal reasoning capabilities as the first framework of its kind operating autonomously without relying on external tools or modules during both the causal learning and reasoning phases. To evaluate the performance of C2P, we first demonstrate that reasoning accuracy improved by over 30.7% and 25.9% for GPT-4 Turbo and LLaMA 3.1, respectively, when using our framework, compared to the same models without C2P on a synthetic benchmark dataset. Then, using few-shot learning of the same LLMs with C2P, the reasoning accuracy increased by more than 20.05% and 20.89%, respectively, with as few as ten examples, compared to the corresponding LLMs without C2P on the same dataset. To evaluate C2P in realistic scenarios, we utilized another benchmark dataset containing natural stories across various fields, including healthcare, medicine, economics, education, social sciences, environmental science, and marketing. The results show improved reasoning when C2P is applied, compared to cases where our framework is not used, which often leads to random and hallucinated responses. By showing the improved performance of few-shot learned GPT-4 Turbo and LLaMA 3.1 with C2P, we demonstrate the generalizability of our framework.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Placed in Causal reasoning in LLMs, one of 17 directions in LLM reasoning. Builds on 41 of its 59 references that are in the map. First posted on arXiv 25 Jul 2024.

Builds on

41 of 59 references.

Closest ancestors · 4

Same direction, newest first

  1. 2024ALCM: Autonomous LLM-Augmented Causal Discovery Framework2 May
  2. 2024Are LLMs Capable of Data-based Statistical and Causal Reasoning? Benchmarking Advanced Quantitative Reasoning with Data27 Feb
  3. 2023CLadder: Assessing Causal Reasoning in Language Models7 Dec
  4. 2023Causal Reasoning and Large Language Models: Opening a New Frontier for Causality28 Apr

Other references · 37

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2023GPT-4 Technical Reportcited by 18,132
  4. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  5. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  6. 2020Scaling Laws for Neural Language Modelscited by 6,411
  7. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  8. 2022Scaling Instruction-Finetuned Language Modelscited by 2,997
  9. 2022Emergent Abilities of Large Language Modelscited by 2,483
  10. 2019Language Models as Knowledge Bases?cited by 1,922
  11. 2022Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?cited by 1,413
  12. 2016ConceptNet 5.5: An Open Multilingual Graph of General Knowledgecited by 1,065
25 more
  1. 2019How Can We Know What Language Models Know?cited by 953
  2. 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 745
  3. 2024Hallucination is Inevitable: An Innate Limitation of Large Language Modelscited by 329
  4. 2023MathPrompter: Mathematical Reasoning using Large Language Modelscited by 236
  5. 2024Foundational Challenges in Assuring Alignment and Safety of Large Language Modelscited by 193
  6. 2020On the Role of Sparsity and DAG Constraints for Learning Linear DAGscited by 186
  7. 2013Causal Inference in the Presence of Latent Variables and Selection Biascited by 165
  8. 2023Causal Parrots: Large Language Models May Talk Causality But Are Not Causalcited by 146
  9. 2023Can Large Language Models Infer Causation from Correlation?cited by 142
  10. 2022DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterizationcited by 135
  11. 2023Hypothesis Search: Inductive Reasoning with Language Modelscited by 119
  12. 2023Calibrated Language Models Must Hallucinatecited by 114
  13. 2023Nonparametric Identifiability of Causal Representations from Unknown Interventionscited by 105
  14. 2023LLMs and the Abstraction and Reasoning Corpus: Successes, Failures, and the Importance of Object-based Representationscited by 81
  15. 2015External Validity: From Do-Calculus to Transportability Across Populationscited by 75
  16. 2020Pre-trained Summarization Distillationcited by 70
  17. 2024Alice in Wonderland: Simple Tasks Showing Complete Reasoning Breakdown in State-Of-the-Art Large Language Modelscited by 67
  18. 2024Efficient Causal Graph Discovery Using Large Language Modelscited by 51
  19. 2022Causal Structure Learning: a Combinatorial Perspectivecited by 46
  20. 2023CRAB: Assessing the Strength of Causal Relationships Between Real-world Eventscited by 27
  21. 2024Cause and Effect: Can Large Language Models Truly Understand Causality?cited by 23
  22. 2023Optimizing NOTEARS Objectives via Topological Swapscited by 17
  23. 2023Towards Causal Foundation Model: on Duality between Causal Inference and Attentioncited by 17
  24. 2023Subset verification and search algorithms for causal DAGscited by 12
  25. 2023Strings from the Library of Babel: Random Sampling as a Strong Baseline for Prompt Optimisationcited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 4; 2026: 2.

8 papers.

  1. 2024CausalEval: Towards Better Causal Reasoning in Language Modelscited by 13
  2. 2025Mitigating Hallucinations in Large Language Models via Causal Reasoningcited by 13
  3. 2024Multimodal Causal Reasoning Benchmark: Challenging Vision Large Language Models to Discern Causal Links Across Modalitiescited by 5
  4. 2025Causal Reasoning in Pieces: Modular In-Context Learning for Causal Discoverycited by 3
  5. 2025Causality Is Key to Understand and Balance Multiple Goals in Trustworthy ML and Foundation Modelscited by 2
  6. 2025CRAwDAD: Causal Reasoning Augmentation with Dual-Agent Debatecited by 1
  7. 2026Trustworthy AI Suffers from Invariance Conflicts and Causality is The Solutioncited by 1
  8. 2026Externalized CPDAG Summaries Improve LLM Causal Deductioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024CARL-GT: Evaluating Causal Reasoning Capabilities of Large Language Models
  2. 2025Unveiling Causal Reasoning in Large Language Models: Reality or Mirage?
  3. 2024Prompting Strategies for Enabling Large Language Models to Infer Causation from Correlation
  4. 2025A Survey on Enhancing Causal Reasoning Ability of Large Language Models
  5. 2026Debiasing Large Language Models via Adaptive Causal Prompting with Sketch-of-Thought
  6. 2024Eliciting Causal Abilities in Large Language Models for Reasoning Tasks
  7. 2024A Critical Review of Causal Reasoning Benchmarks for Large Language Models
  8. 2025ExpliCa: Evaluating Explicit Causal Reasoning in Large Language Models