mlsanityData to 14 Oct 2026

LLM4Causal: Democratized Causal Tools for Everyone via Large Language Model

arXiv 2312.17122 ↗First posted 28 Dec 2023cs.CL cs.AI stat.MLcited by 9

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

Abstract (arXiv)

Large Language Models (LLMs) have shown their success in language understanding and reasoning on general topics. However, their capability to perform inference based on user-specified structured data and knowledge in corpus-rare concepts, such as causal decision-making is still limited. In this work, we explore the possibility of fine-tuning an open-sourced LLM into LLM4Causal, which can identify the causal task, execute a corresponding function, and interpret its numerical results based on users' queries and the provided dataset. Meanwhile, we propose a data generation process for more controllable GPT prompting and present two instruction-tuning datasets: (1) Causal-Retrieval-Bench for causal problem identification and input parameter extraction for causal function calling and (2) Causal-Interpret-Bench for in-context causal interpretation. By conducting end-to-end evaluations and two ablation studies, we showed that LLM4Causal can deliver end-to-end solutions for causal problems and provide easy-to-understand answers, which significantly outperforms the baselines.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use. Builds on 39 of its 58 references that are in the map. First posted on arXiv 28 Dec 2023.

Builds on

39 of 58 references.

References · 39

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27 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 5; 2025: 2; 2026: 2.

9 papers.

  1. 2024Integrating Large Language Models in Causal Discovery: A Statistical Causal Approachcited by 32
  2. 2024Large Language Models for Causal Discovery: Current Landscape and Future Directionscited by 25
  3. 2024Causality for Large Language Modelscited by 25
  4. 2024CausalEval: Towards Better Causal Reasoning in Language Modelscited by 13
  5. 2025CausalAbstain: Enhancing Multilingual LLMs with Causal Reasoning for Trustworthy Abstentioncited by 10
  6. 2026How Far Are LLMs from Professional Poker Players? Revisiting Game-Theoretic Reasoning with Agentic Tool Usecited by 7
  7. 2024Building Decision Making Models Through Language Model Regimecited by 2
  8. 2026NoisyCausal: A Benchmark for Evaluating Causal Reasoning Under Structured Noisecited by 2
  9. 2025CRAwDAD: Causal Reasoning Augmentation with Dual-Agent Debatecited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2024Large Language Models and Causal Inference in Collaboration: A Survey
  2. 2024A Critical Review of Causal Reasoning Benchmarks for Large Language Models
  3. 2024CausalBN-Bench: A Comprehensive Benchmark for Causal Learning Capability of LLMs
  4. 2024Causal Inference with Large Language Model: A Survey
  5. 2024Evaluating Interventional Reasoning Capabilities of Large Language Models
  6. 2025A Survey on Enhancing Causal Reasoning Ability of Large Language Models
  7. 2023Can Large Language Models Infer Causation from Correlation?
  8. 2023CLadder: Assessing Causal Reasoning in Language Models