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Causal inference has been a pivotal challenge across diverse domains such as medicine and economics, demanding a complicated integration of human knowledge, mathematical reasoning, and data mining capabilities.
Language models are few-shot learners
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The estimation of causal effects from observational data
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Causation, prediction, and search
Peter Spirtes, Clark N Glymour, and Richard Scheines. 2000 · 2000
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Learning equivalence classes of bayesian-network structures
David Maxwell Chickering. 2002 · 2002
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Causal discovery with prior information
Rodney T O’Donnell, Ann E Nicholson, Bin Han, Kevin B Korb, M Jahangir Alam, and Lucas R Hope. 2006 · 2006
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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 · 2006
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Causality
Judea Pearl. 2009 · 2009
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Choice of plausible alternatives: An evaluation of commonsense causal reasoning
Melissa Roemmele, Cosmin Adrian Bejan, and Andrew S Gordon. 2011 · 2011
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Causal inference in public health
Thomas A Glass, Steven N Goodman, Miguel A Hernán, and Jonathan M Samet. 2013 · 2013
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Causal inference in the presence of latent variables and selection bias
Peter L Spirtes, Christopher Meek, and Thomas S Richardson. 2013 · 2013
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Causal inference in statistics, social, and biomedical sciences
Guido W Imbens and Donald B Rubin. 2015 · 2015
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Shock-based causal inference in corporate finance and accounting research
Vladimir A Atanasov and Bernard S Black. 2016 · 2016
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Commonsense causal reasoning between short texts
Zhiyi Luo, Yuchen Sha, Kenny Q Zhu, Seung-won Hwang, and Zhongyuan Wang. 2016 · 2016
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Distinguishing cause from effect using observational data: methods and benchmarks
Joris M Mooij, Jonas Peters, Dominik Janzing, Jakob Zscheischler, and Bernhard Schölkopf. 2016 · 2016
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Causal discovery and inference: concepts and recent methodological advances
Peter Spirtes and Kun Zhang. 2016 · 2016
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Bayesian network structure learning with side constraints
Andrew Li and Peter Beek. 2018 · 2018
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The book of why: the new science of cause and effect
Judea Pearl and Dana Mackenzie. 2018 · 2018
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Dags with no tears: Continuous optimization for structure learning
Xun Zheng, Bryon Aragam, Pradeep K Ravikumar, and Eric P Xing. 2018 · 2018
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Neuropathic pain diagnosis simulator for causal discovery algorithm evaluation
Ruibo Tu, Kun Zhang, Bo Bertilson, Hedvig Kjellstrom, and Cheng Zhang. 2019 · 2019
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Dag-gnn: Dag structure learning with graph neural networks
Yue Yu, Jie Chen, Tian Gao, and Mo Yu. 2019 · 2019
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Investigating gender bias in language models using causal mediation analysis
Jesse Vig, Sebastian Gehrmann, Yonatan Belinkov, Sharon Qian, Daniel Nevo, Yaron Singer, and Stuart Shieber. 2020 · 2020
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Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2021 · 2021
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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 · 2021
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Guided generation of cause and effect
Zhongyang Li, Xiao Ding, Ting Liu, J Edward Hu, and Benjamin Van Durme. 2021 · 2021
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A survey on causal inference
Liuyi Yao, Zhixuan Chu, Sheng Li, Yaliang Li, Jing Gao, and Aidong Zhang. 2021 · 2021
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On pearl’s hierarchy and the foundations of causal inference
Elias Bareinboim, Juan D Correa, Duligur Ibeling, and Thomas Icard. 2022 · 2022
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Causalqa: A benchmark for causal question answering
Alexander Bondarenko, Magdalena Wolska, Stefan Heindorf, Lukas Blübaum, Axel-Cyrille Ngonga Ngomo, Benno Stein, Pavel Braslavski, Matthias Hagen, and Martin Potthast. 2022 · 2022
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e-care: a new dataset for exploring explainable causal reasoning
Li Du, Xiao Ding, Kai Xiong, Ting Liu, and Bing Qin. 2022 · 2022
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Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al. 2023 · 2023
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Causal reasoning and large language models: Opening a new frontier for causality
Emre Kıcıman, Robert Ness, Amit Sharma, and Chenhao Tan. 2023 · 2023
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Text-transport: Toward learning causal effects of natural language
Victoria Lin, Louis-Philippe Morency, and Eli Ben-Michael. 2023 · 2023
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Trustworthy llms: a survey and guideline for evaluating large language models’ alignment
Yang Liu, Yuanshun Yao, Jean-Francois Ton, Xiaoying Zhang, Ruocheng Guo Hao Cheng, Yegor Klochkov, Muhammad Faaiz Taufiq, and Hang Li. 2023 · 2023
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Causal inference in natural language processing: Estimation, prediction, interpretation and beyond
Amir Feder, Katherine A Keith, Emaad Manzoor, Reid Pryzant, Dhanya Sridhar, Zach Wood-Doughty, Jacob Eisenstein, Justin Grimmer, Roi Reichart, Margaret E Roberts, et al. 2022 · 2022
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Crass: A novel data set and benchmark to test counterfactual reasoning of large language models
Jörg Frohberg and Frank Binder. 2022 · 2022
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Wikiwhy: Answering and explaining cause-and-effect questions
Matthew Ho, Aditya Sharma, Justin Chang, Michael Saxon, Sharon Levy, Yujie Lu, and William Yang Wang. 2022 · 2022
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Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022 · 2022
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Locating and editing factual associations in gpt
Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov. 2022 · 2022
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Methods and tools for causal discovery and causal inference
Ana Rita Nogueira, Andrea Pugnana, Salvatore Ruggieri, Dino Pedreschi, and João Gama. 2022 · 2022
Cited alongside, same era.
Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022 · 2022
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Stephanie Long, Tibor Schuster, Alexandre Piché, ServiceNow Research, et al. 2023 · 2023
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Model card and evaluations for claude models
C. Models. 2023 · 2023
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Introducing chatgpt
OpenAI. 2022 · 2023
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Answering causal questions with augmented llms
Nick Pawlowski, James Vaughan, Joel Jennings, and Cheng Zhang. 2023 · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. 2023 · 2023
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Causal-discovery performance of chatgpt in the context of neuropathic pain diagnosis
Ruibo Tu, Chao Ma, and Cheng Zhang. 2023 · 2023
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Causal parrots: Large language models may talk causality but are not causal
Matej Zečević, Moritz Willig, Devendra Singh Dhami, and Kristian Kersting. 2023 · 2023
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Causality in the time of llms: Round table discussion results of clear 2023
Cheng Zhang, Dominik Janzing, Mihaela van der Schaar, Francesco Locatello, and Peter Spirtes. 2023 · 2023
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Causal question answering with reinforcement learning
Lukas Blübaum and Stefan Heindorf. 2024 · 2024
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Causal agent based on large language model
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Large language models and causal inference in collaboration: A comprehensive survey
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Causal-copilot: An autonomous causal analysis agent
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Fusing llms and kgs for formal causal reasoning behind financial risk contagion
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