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Causal discovery (CD) and Large Language Models (LLMs) have emerged as transformative fields in artificial intelligence that have evolved largely independently.
Local computations with probabilities on graphical structures and their application to expert systems
Judea Pearl · 1988
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Adaptive probabilistic networks with hidden variables
John Binder, Daphne Koller, Stuart Russell, and Keiji Kanazawa · 1997
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Causation, prediction, and search
Peter Spirtes, Clark Glymour, and Richard Scheines · 2001
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A linear non-gaussian acyclic model for causal discovery
Shohei Shimizu, Patrik O Hoyer, Aapo Hyvärinen, Antti Kerminen, and Michael Jordan · 2006
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The max-min hill-climbing bayesian network structure learning algorithm
Ioannis Tsamardinos, Laura E. Brown, and Constantin F. Aliferis · 2006
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Causal inference in statistics: An overview
Judea Pearl · 2009
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Directlingam: A direct method for learning a linear non-gaussian structural equation model, 2011
Shohei Shimizu, Takanori Inazumi, Yasuhiro Sogawa, Aapo Hyvarinen, Yoshinobu Kawahara, Takashi Washio, Patrik O. Hoyer, and Kenneth Bollen · 2011
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Bayesian Networks: With Examples in R
Marco Scutari and Jean-Baptiste Denis · 2014
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Structure learning in graphical modeling
Mathias Drton and Marloes H Maathuis · 2017
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Elements of Causal Inference - Foundations and Learning Algorithms
J. Peters, D. Janzing, and B. Schölkopf · 2017
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Dags with no tears: Continuous optimization for structure learning, 2018
Xun Zheng, Bryon Aragam, Pradeep Ravikumar, and Eric P. Xing · 2018
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Review of causal discovery methods based on graphical models
Clark Glymour, Kun Zhang, and Peter Spirtes · 2019
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Causenet: Towards a causality graph extracted from the web
Stefan Heindorf, Yan Scholten, Henning Wachsmuth, Axel-Cyrille Ngonga Ngomo, and Martin Potthast · 2020
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Causal inference
Kun Kuang, Lian Li, Zhi Geng, Lei Xu, Kun Zhang, Beishui Liao, Huaxin Huang, Peng Ding, Wang Miao, and Zhichao Jiang · 2020
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Benchmarking of data-driven causality discovery approaches in the interactions of arctic sea ice and atmosphere
Yiyi Huang, Matthäus Kleindessner, Alexey Munishkin, Debvrat Varshney, Pei Guo, and Jianwu Wang · 2021
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Chain of thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, brian ichter, Fei Xia, Ed H. Chi, Quoc V Le, and Denny Zhou · 2022
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Can foundation models talk causality?, 2022
Moritz Willig, Matej Zečević, Devendra Singh Dhami, and Kristian Kersting · 2022
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Causal structure learning supervised by large language model, 2023
Taiyu Ban, Lyuzhou Chen, Derui Lyu, Xiangyu Wang, and Huanhuan Chen · 2023
Cited alongside, same era.
From query tools to causal architects: Harnessing large language models for advanced causal discovery from data, 2023
Taiyu Ban, Lyvzhou Chen, Xiangyu Wang, and Huanhuan Chen · 2023
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Mitigating prior errors in causal structure learning: Towards llm driven prior knowledge, 2023
Lyuzhou Chen, Taiyu Ban, Xiangyu Wang, Derui Lyu, and Huanhuan Chen · 2023
Cited alongside, same era.
Task-driven causal feature distillation: Towards trustworthy risk prediction
Zhixuan Chu, Mengxuan Hu, Qing Cui, Longfei Li, and Sheng Li · 2023
Cited alongside, same era.
Large language models for constrained-based causal discovery
Kai-Hendrik Cohrs, Emiliano Diaz, Vasileios Sitokonstantinou, Gherardo Varando, and Gustau Camps-Valls · 2023
Cited alongside, same era.
From pre-training corpora to large language models: What factors influence llm performance in causal discovery tasks?, 2024
Tao Feng, Lizhen Qu, Niket Tandon, Zhuang Li, Xiaoxi Kang, and Gholamreza Haffari · 2024
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Language agents meet causality – bridging llms and causal world models, 2024
John Gkountouras, Matthias Lindemann, Phillip Lippe, Efstratios Gavves, and Ivan Titov · 2024
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LLM4causal: Democratized causal tools for everyone via large language model
Haitao Jiang, Lin Ge, Yuhe Gao, Jianian Wang, and Rui Song · 2024
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Efficient causal graph discovery using large language models, 2024
Thomas Jiralerspong, Xiaoyin Chen, Yash More, Vedant Shah, and Yoshua Bengio · 2024
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Llm-initialized differentiable causal discovery, 2024
Shiv Kampani, David Hidary, Constantijn van der Poel, Martin Ganahl, and Brenda Miao · 2024
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Survey of hallucination in natural language generation
Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung · 2023
Cited alongside, same era.
CLadder: Assessing causal reasoning in language models
Zhijing Jin, Yuen Chen, Felix Leeb, Luigi Gresele, Ojasv Kamal, Zhiheng Lyu, Kevin Blin, Fernando Gonzalez, Max Kleiman-Weiner, Mrinmaya Sachan, and Bernhard Schölkopf · 2023
Cited alongside, same era.
Can large language models infer causation from correlation?, 2023
Zhijing Jin, Jiarui Liu, Zhiheng Lyu, Spencer Poff, Mrinmaya Sachan, Rada Mihalcea, Mona Diab, and Bernhard Schölkopf · 2023
Cited alongside, same era.
Causal reasoning and large language models: Opening a new frontier for causality, 2023
Emre Kıcıman, Robert Ness, Amit Sharma, and Chenhao Tan · 2023
Cited alongside, same era.
Causal discovery with language models as imperfect experts
Stephanie Long, Alexandre Piché, Valentina Zantedeschi, Tibor Schuster, and Alexandre Drouin · 2023
Cited alongside, same era.
Causal-discovery performance of chatgpt in the context of neuropathic pain diagnosis, 2023
Ruibo Tu, Chao Ma, and Cheng Zhang · 2023
Cited alongside, same era.
Causal inference using llm-guided discovery, 2023
Aniket Vashishtha, Abbavaram Gowtham Reddy, Abhinav Kumar, Saketh Bachu, Vineeth N Balasubramanian, and Amit Sharma · 2023
Cited alongside, same era.
Hao Duong Le, Xin Xia, and Zhang Chen · 2024
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Discovery of the hidden world with large language models, 2024
Chenxi Liu, Yongqiang Chen, Tongliang Liu, Mingming Gong, James Cheng, Bo Han, and Kun Zhang · 2024
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Can large language models build causal graphs?, 2024
Stephanie Long, Tibor Schuster, and Alexandre Piché · 2024
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Metaphorprompt - an analogical reasoning approach for extracting causal links from biological text
Parth Patel, Yu-Chiao Chiu, Yufei Hunag, and Jianqiu Zhang · 2024
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Exploring multi-modal integration with tool-augmented llm agents for precise causal discovery, 2024
ChengAo Shen, Zhengzhang Chen, Dongsheng Luo, Dongkuan Xu, Haifeng Chen, and Jingchao Ni · 2024
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Towards automating causal discovery in financial markets and beyond
Alik Sokolov, Fabrizzio Sabelli, Behzad faraz, Wuding Li, and Luis Seco · 2024
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Integrating large language models in causal discovery: A statistical causal approach, 2024
Masayuki Takayama, Tadahisa Okuda, Thong Pham, Tatsuyoshi Ikenoue, Shingo Fukuma, Shohei Shimizu, and Akiyoshi Sannai · 2024
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Improving causal reasoning in large language models: A survey, 2024
Longxuan Yu, Delin Chen, Siheng Xiong, Qingyang Wu, Qingzhen Liu, Dawei Li, Zhikai Chen, Xiaoze Liu, and Liangming Pan · 2024
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Causal graph discovery with retrieval-augmented generation based large language models, 2024
Yuzhe Zhang, Yipeng Zhang, Yidong Gan, Lina Yao, and Chen Wang · 2024
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Causalbench: A comprehensive benchmark for causal learning capability of llms, 2024
Yu Zhou, Xingyu Wu, Beicheng Huang, Jibin Wu, Liang Feng, and Kay Chen Tan · 2024
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Large language models for causal hypothesis generation in science
Kai-Hendrik Cohrs, Emiliano Diaz, Vasileios Sitokonstantinou, Gherardo Varando, and Gustau Camps-Valls · 2025
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