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Causal reasoning is one of the primary bottlenecks that Large Language Models (LLMs) must overcome to attain human-level intelligence.
The direction of time , volume 65
Hans Reichenbach · 1991
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Causal diagrams for empirical research
Judea Pearl · 1995
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Causal inference in the presence of latent variables and selection bias
Peter Spirtes, Christopher Meek, and Thomas Richardson · 1995
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Causation, prediction, and search
Peter Spirtes, Clark Glymour, and Richard Scheines · 2001
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Optimal structure identification with greedy search
David Maxwell Chickering · 2002
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Instruments for causal inference: an epidemiologist’s dream?
Miguel A Hernán and James M Robins · 2006
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Causal cognition in human and nonhuman animals: A comparative, critical review
Derek C Penn and Daniel J Povinelli · 2007
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Characterization and greedy learning of interventional markov equivalence classes of directed acyclic graphs
Alain Hauser and Peter Bühlmann · 2012
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Sample size calculations in clinical research
Shein-Chung Chow, Jun Shao, Hansheng Wang, and Yuliya Lokhnygina · 2017
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Experimental design for learning causal graphs with latent variables
Murat Kocaoglu, Karthikeyan Shanmugam, and Elias Bareinboim · 2017
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Conceptnet 5.5: An open multilingual graph of general knowledge
Robyn Speer, Joshua Chin, and Catherine Havasi · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin · 2018
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The book of why: the new science of cause and effect
Judea Pearl and Dana Mackenzie · 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
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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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Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer · 2019
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Language models as knowledge bases?
Fabio Petroni, Tim Rocktäschel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, Alexander H Miller, and Sebastian Riedel · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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How can we know what language models know?
Zhengbao Jiang, Frank F Xu, Jun Araki, and Graham Neubig · 2020
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Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2020
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On the role of sparsity and dag constraints for learning linear dags
Ignavier Ng, AmirEmad Ghassami, and Kun Zhang · 2020
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Identifying causal effects in maximally oriented partially directed acyclic graphs
Emilija Perkovic · 2020
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Pre-trained summarization distillation
Sam Shleifer and Alexander M Rush · 2020
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Stanford alpaca: An instruction-following llama model, 2023
Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B Hashimoto · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
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Hypothesis search: Inductive reasoning with language models
Ruocheng Wang, Eric Zelikman, Gabriel Poesia, Yewen Pu, Nick Haber, and Noah D Goodman · 2023
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Yudong Xu, Wenhao Li, Pashootan Vaezipoor, Scott Sanner, and Elias B Khalil · 2023
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Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, et al · 2020
Cited alongside, same era.
On Pearl’s Hierarchy and the Foundations of Causal Inference , pp. 507–556
Elias Bareinboim, Juan D. Correa, Duligur Ibeling, and Thomas Icard · 2022
Cited alongside, same era.
Dagma: Learning dags via m-matrices and a log-determinant acyclicity characterization
Kevin Bello, Bryon Aragam, and Pradeep Ravikumar · 2022
Cited alongside, same era.
Investigating causal understanding in llms
Marius Hobbhahn, Tom Lieberum, and David Seiler · 2022
Cited alongside, same era.
External Validity: From Do-Calculus to Transportability Across Populations , pp. 451–482
Judea Pearl and Elias Bareinboim · 2022
Cited alongside, same era.
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
Cited alongside, same era.
Subset verification and search algorithms for causal dags
Davin Choo and Kirankumar Shiragur · 2023
Cited alongside, same era.
Matej Zečević, Moritz Willig, Devendra Singh Dhami, and Kristian Kersting · 2023
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Towards causal foundation model: on duality between causal inference and attention
Jiaqi Zhang, Joel Jennings, Cheng Zhang, and Chao Ma · 2023
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Foundational challenges in assuring alignment and safety of large language models
Usman Anwar, Abulhair Saparov, Javier Rando, Daniel Paleka, Miles Turpin, Peter Hase, Ekdeep Singh Lubana, Erik Jenner, Stephen Casper, Oliver Sourbut, et al · 2024
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Cause and effect: Can large language models truly understand causality?
Swagata Ashwani, Kshiteesh Hegde, Nishith Reddy Mannuru, Mayank Jindal, Dushyant Singh Sengar, Krishna Chaitanya Rao Kathala, Dishant Banga, Vinija Jain, and Aman Chadha · 2024
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Llms with chain-of-thought are non-causal reasoners
Guangsheng Bao, Hongbo Zhang, Linyi Yang, Cunxiang Wang, and Yue Zhang · 2024
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Causalquest: Collecting natural causal questions for ai agents
Roberto Ceraolo, Dmitrii Kharlapenko, Amélie Reymond, Rada Mihalcea, Mrinmaya Sachan, Bernhard Schölkopf, and Zhijing Jin · 2024
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Scaling instruction-finetuned language models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al · 2024
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Causal-structure driven augmentations for text ood generalization
Amir Feder, Yoav Wald, Claudia Shi, Suchi Saria, and David Blei · 2024
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Efficient causal graph discovery using large language models
Thomas Jiralerspong, Xiaoyin Chen, Yash More, Vedant Shah, and Yoshua Bengio · 2024
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Alcm: Autonomous llm-augmented causal discovery framework
Elahe Khatibi, Mahyar Abbasian, Zhongqi Yang, Iman Azimi, and Amir M Rahmani · 2024
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Strings from the library of babel: Random sampling as a strong baseline for prompt optimisation
Yao Lu, Jiayi Wang, Raphael Tang, Sebastian Riedel, and Pontus Stenetorp · 2024
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Marianna Nezhurina, Lucia Cipolina-Kun, Mehdi Cherti, and Jenia Jitsev · 2024
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Nonparametric identifiability of causal representations from unknown interventions
Julius von Kügelgen, Michel Besserve, Liang Wendong, Luigi Gresele, Armin Kekić, Elias Bareinboim, David Blei, and Bernhard Schölkopf · 2024
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Hallucination is inevitable: An innate limitation of large language models
Ziwei Xu, Sanjay Jain, and Mohan Kankanhalli · 2024
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