Fetching the paper…
Reading the bibliography…
Causal structure discovery methods are commonly applied to structured data where the causal variables are known and where statistical testing can be used to assess the causal relationships.
A density-based algorithm for discovering clusters in large spatial databases with noise
Martin Ester, Hans-Peter Kriegel, Jörg Sander, Xiaowei Xu, et al · 1996
Earlier work this paper cites.
Causality
Judea Pearl · 2009
Earlier work this paper cites.
Event registry: learning about world events from news
Gregor Leban, Blaz Fortuna, Janez Brank, and Marko Grobelnik · 2014
Earlier work this paper cites.
Analogical abduction and prediction: Their impact on deception
Kenneth D. Forbus · 2015
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
Earlier work this paper cites.
Strategic foresight primer
Angela Wilkinson · 2017
Earlier work this paper cites.
Graph attention networks
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
Earlier work this paper cites.
BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
Earlier work this paper cites.
Crystal cube: Multidisciplinary approach to disruptive events prediction
Nathan H Parrish, Anna L Buczak, Jared T Zook, James P Howard, Brian J Ellison, and Benjamin D Baugher · 2019
Earlier work this paper cites.
How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
Earlier work this paper cites.
Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
Earlier work this paper cites.
Rigidities of imagination in scenario planning: Strategic foresight through ‘unlearning’
George Burt and Anup Karath Nair · 2020
Earlier work this paper cites.
Foresight-based leadership. decision-making in a growing ai environment
Norbert Reez · 2020
Earlier work this paper cites.
Foundations of structural causal models with cycles and latent variables
Stephan Bongers, Patrick Forré, Jonas Peters, and Joris M. Mooij · 2021
Earlier work this paper cites.
Artificial intelligence in strategic foresight–current practices and future application potentials: Current practices and future application potentials
Patrick Brandtner and Marius Mates · 2021
Earlier work this paper cites.
Sensemaking and lens-shaping: Identifying citizen contributions to foresight through comparative topic modelling
Aaron B Rosa, Niklas Gudowsky, and Petteri Repo · 2021
Earlier work this paper cites.
Toward causal representation learning
Bernhard Schölkopf, Francesco Locatello, Stefan Bauer, Nan Rosemary Ke, Nal Kalchbrenner, Anirudh Goyal, and Yoshua Bengio · 2021
Earlier work this paper cites.
On pearl’s hierarchy and the foundations of causal inference
Elias Bareinboim, Juan D. Correa, Duligur Ibeling, and Thomas Icard · 2022
Earlier work this paper cites.
Detecting emerging technologies and their evolution using deep learning and weak signal analysis
Ashkan Ebadi, Alain Auger, and Yvan Gauthier · 2022
Cited alongside, same era.
New perspectives for data-supported foresight: The hybrid ai-expert approach
Amber Geurts, Ralph Gutknecht, Philine Warnke, Arjen Goetheer, Elna Schirrmeister, Babette Bakker, and Svetlana Meissner · 2022
Cited alongside, same era.
Causal inference through the structural causal marginal problem
Luigi Gresele, Julius von Kügelgen, Jonas M. Kübler, Elke Kirschbaum, Bernhard Schölkopf, and Dominik Janzing · 2022
Cited alongside, same era.
Investigating causal understanding in llms
Marius Hobbhahn, Tom Lieberum, and David Seiler · 2022
Cited alongside, same era.
Causal transformer for estimating counterfactual outcomes
Valentyn Melnychuk, Dennis Frauen, and Stefan Feuerriegel · 2022
Cited alongside, same era.
Analogy as a search procedure: a dimensional view
Ai-based strategic foresight for environment protection
Jože M Rožanec, Radu Prodan, Dumitru Roman, Gregor Leban, and Marko Grobelnik · 2023
Later among the works it cites.
Zhaofeng Wu, Linlu Qiu, Alexis Ross, Ekin Akyürek, Boyuan Chen, Bailin Wang, Najoung Kim, Jacob Andreas, and Yoon Kim · 2023
Later among the works it cites.
Tree of thoughts: Deliberate problem solving with large language models
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, and Karthik Narasimhan · 2023
Later among the works it cites.
Causal parrots: Large language models may talk causality but are not causal
Matej Zecevic, Moritz Willig, Devendra Singh Dhami, and Kristian Kersting · 2023
Later among the works it cites.
End-to-end causal effect estimation from unstructured natural language data
Nikita Dhawan, Leonardo Cotta, Karen Ullrich, Rahul G. Krishnan, and Chris J. Maddison · 2024
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Matías Osta-Vélez and Peter Gärdenfors · 2022
Cited alongside, same era.
Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul F. Christiano, Jan Leike, and Ryan Lowe · 2022
Cited alongside, same era.
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
Cited alongside, same era.
ROCK: causal inference principles for reasoning about commonsense causality
Jiayao Zhang, Hongming Zhang, Weijie J. Su, and Dan Roth · 2022
Cited alongside, same era.
Sparks of artificial general intelligence: Early experiments with GPT-4
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott M. Lundberg, Harsha Nori, Hamid Palangi, Marco Túlio Ribeiro, and Yi Zhang · 2023
Cited alongside, same era.
Lei Huang, Weijiang Yu, Weitao Ma, Weihong Zhong, Zhangyin Feng, Haotian Wang, Qianglong Chen, Weihua Peng, Xiaocheng Feng, Bing Qin, and Ting Liu · 2023
Cited alongside, same era.
Cladder: A benchmark to assess causal reasoning capabilities of language models
Zhijing Jin, Yuen Chen, Felix Leeb, Luigi Gresele, Ojasv Kamal, Zhiheng Lyu, Kevin Blin, Fernando Gonzalez Adauto, Max Kleiman-Weiner, Mrinmaya Sachan, and Bernhard Schölkopf · 2023
Cited alongside, same era.
Closest in time.
Large language models are not strong abstract reasoners
Gaël Gendron, Qiming Bao, Michael Witbrock, and Gillian Dobbie · 2024
Closest in time.
Causality extraction from medical text using large language models (llms)
Seethalakshmi Gopalakrishnan, Luciana Garbayo, and Wlodek Zadrozny · 2024
Closest in time.
Can large language models infer causation from correlation?
Zhijing Jin, Jiarui Liu, Zhiheng Lyu, Spencer Poff, Mrinmaya Sachan, Rada Mihalcea, Mona T. Diab, and Bernhard Schölkopf · 2024
Closest in time.
Efficient causal graph discovery using large language models
Thomas Jiralerspong, Xiaoyin Chen, Yash More, Vedant Shah, and Yoshua Bengio · 2024
Closest in time.
Llms are prone to fallacies in causal inference
Nitish Joshi, Abulhair Saparov, Yixin Wang, and He He · 2024
Closest in time.
Causal inference with large language model: A survey
Jing Ma · 2024
Closest in time.
Hello gpt-4o, 2024
OpenAI · 2024
Closest in time.
Phenomenal yet puzzling: Testing inductive reasoning capabilities of language models with hypothesis refinement
Linlu Qiu, Liwei Jiang, Ximing Lu, Melanie Sclar, Valentina Pyatkin, Chandra Bhagavatula, Bailin Wang, Yoon Kim, Yejin Choi, Nouha Dziri, and Xiang Ren · 2024
Closest in time.
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
Machel Reid, Nikolay Savinov, Denis Teplyashin, Dmitry Lepikhin, Timothy P. Lillicrap, Jean-Baptiste Alayrac, Radu Soricut, Angeliki Lazaridou, Orhan Firat, Julian Schrittwieser, Ioannis Antonoglou, Rohan Anil, Sebastian Borgeaud, Andrew M. Dai, Katie Millican, Ethan Dyer, Mia Glaese, Thibault Sottiaux, Benjamin Lee, Fabio Viola, Malcolm Reynolds, Yuanzhong Xu, James Molloy, Jilin Chen, Michael Isard, Paul Barham, Tom Hennigan, Ross McIlroy, Melvin Johnson, Johan Schalkwyk, Eli Collins, Eliza Rutherford, Erica Moreira, Kareem Ayoub, Megha Goel, Clemens Meyer, Gregory Thornton, Zhen Yang, Henryk Michalewski, Zaheer Abbas, Nathan Schucher, Ankesh Anand, Richard Ives, James Keeling, Karel Lenc, Salem Haykal, Siamak Shakeri, Pranav Shyam, Aakanksha Chowdhery, Roman Ring, Stephen Spencer, Eren Sezener, and et al · 2024
Closest in time.
Back to the future: predicting causal relationships influencing oil prices
Jose Rozanec, Beno Šircelj, Michael Cochez, and Gregor Leban · 2024
Closest in time.
Alphazero-like tree-search can guide large language model decoding and training
Ziyu Wan, Xidong Feng, Muning Wen, Stephen Marcus McAleer, Ying Wen, Weinan Zhang, and Jun Wang · 2024
Closest in time.
Self-rewarding language models
Weizhe Yuan, Richard Yuanzhe Pang, Kyunghyun Cho, Sainbayar Sukhbaatar, Jing Xu, and Jason Weston · 2024
Closest in time.