Fetching the paper…
Reading the bibliography…
Causal structure discovery from observations can be improved by integrating background knowledge provided by an expert to reduce the hypothesis space.
Estimating the dimension of a model
Gideon Schwarz · 1978
Earlier work this paper cites.
Local computations with probabilities on graphical structures and their application to expert systems
Steffen L. Lauritzen and David J. Spiegelhalter · 1988
Earlier work this paper cites.
A bayesian method for the induction of probabilistic networks from data
Gregory F. Cooper and Edward Herskovits · 1992
Earlier work this paper cites.
Learning in probabilistic expert systems
David J. Spiegelhalter · 1992
Earlier work this paper cites.
Causal inference and causal explanation with background knowledge
Christopher Meek · 1995
Earlier work this paper cites.
Adaptive probabilistic networks with hidden variables
John Binder, Daphne Koller, Stuart Russell, and Keiji Kanazawa · 1997
Earlier work this paper cites.
Bayesian Network Repository, 2001
Gal Elidan · 2001
Earlier work this paper cites.
Being Bayesian about network structure. A Bayesian approach to structure discovery in Bayesian networks
Nir Friedman and Daphne Koller · 2003
Earlier work this paper cites.
Ordering-based search: A simple and effective algorithm for learning bayesian networks
Marc Teyssier and Daphne Koller · 2005
Earlier work this paper cites.
Modification of UCT with patterns in Monte-Carlo Go
Sylvain Gelly, Yizao Wang, Rémi Munos, and Olivier Teytaud · 2006
Earlier work this paper cites.
Bandit based Monte-Carlo planning
Levente Kocsis and Csaba Szepesvári · 2006
Earlier work this paper cites.
Structured priors for structure learning
Vikash K. Mansinghka, Charles Kemp, Joshua B. Tenenbaum, and Thomas L. Griffiths · 2006
Earlier work this paper cites.
A linear non-Gaussian acyclic model for causal discovery
Shohei Shimizu, Patrik O. Hoyer, Aapo Hyvärinen, Antti Kerminen, and Michael Jordan · 2006
Earlier work this paper cites.
Bayesian network learning algorithms using structural restrictions
Luis M. de Campos and Javier G. Castellano · 2007
Cited alongside, same era.
Combining online and offline knowledge in UCT
Sylvain Gelly and David Silver · 2007
Cited alongside, same era.
Causality
Judea Pearl · 2009
Cited alongside, same era.
A survey of Monte Carlo tree search methods
Cameron B. Browne, Edward Powley, Daniel Whitehouse, Simon M. Lucas, Peter I. Cowling, Philipp Rohlfshagen, Stephen Tavener, Diego Perez, Spyridon Samothrakis, and Simon Colton · 2012
Cited alongside, same era.
Counterfactuals and Causal Inference
Stephen L. Morgan and Christopher Winship · 2015
Cited alongside, same era.
Learning bayesian networks with ancestral constraints
Eunice Yuh-Jie Chen, Yujia Shen, Arthur Choi, and Adnan Darwiche · 2016
Cited alongside, same era.
Typing assumptions improve identification in causal discovery
Philippe Brouillard, Perouz Taslakian, Alexandre Lacoste, Sébastien Lachapelle, and Alexandre Drouin · 2022
Later among the works it cites.
LMPriors: Pre-trained language models as task-specific priors
Kristy Choi, Chris Cundy, Sanjari Srivastava, and Stefano Ermon · 2022
Later among the works it cites.
Causal BERT: Language models for causality detection between events expressed in text
Vivek Khetan, Roshni Ramnani, Mayuresh Anand, Subhashis Sengupta, and Andrew E. Fano · 2022
Later among the works it cites.
Can large language models build causal graphs?
Stephanie Long, Tibor Schuster, and Alexandre Piché · 2022
Later among the works it cites.
Can foundation models talk causality?
Moritz Willig, Matej Zečević, Devendra Singh Dhami, and Kristian Kersting · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Distinguishing cause from effect using observational data: methods and benchmarks
Joris M. Mooij, Jonas Peters, Dominik Janzing, Jakob Zscheischler, and Bernhard Schölkopf · 2016
Cited alongside, same era.
Mastering the game of Go with deep neural networks and tree search
David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Vedavyas Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy P. Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, and Demis Hassabis · 2016
Cited alongside, same era.
Thinking Fast and Slow with Deep Learning and Tree Search
Thomas Anthony, Zheng Tian, and David Barber · 2017
Cited alongside, same era.
Elements of Causal Inference: Foundations and Learning Algorithms
Jonas Peters, Dominik Janzing, and Bernhard Schölkopf · 2017
Cited alongside, same era.
Mutual information neural estimation
Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeshwar, Sherjil Ozair, Yoshua Bengio, Aaron Courville, and Devon Hjelm · 2018
Cited alongside, same era.
DAGs with no tears: continuous optimization for structure learning
Xun Zheng, Bryon Aragam, Pradeep K. Ravikumar, and Eric P. Xing · 2018
Cited alongside, same era.
Taiyu Ban, Lyvzhou Chen, Xiangyu Wang, and Huanhuan Chen · 2023
Later among the works it cites.
Mistral 7B
Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed · 2023
Later among the works it cites.
Causal reasoning and large language models: Opening a new frontier for causality
Emre Kıcıman, Robert Ness, Amit Sharma, and Chenhao Tan · 2023
Later among the works it cites.
Causal discovery with language models as imperfect experts
Stephanie Long, Alexandre Piché, Valentina Zantedeschi, Tibor Schuster, and Alexandre Drouin · 2023
Later among the works it cites.
On the planning abilities of large language models - a critical investigation
Karthik Valmeekam, Matthew Marquez, Sarath Sreedharan, and Subbarao Kambhampati · 2023
Later among the works it cites.
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.
Introducing Meta Llama 3: The most capable openly available LLM to date, 2024
Meta · 2024
Closest in time.
Causal inference using LLM-guided discovery
Aniket Vashishtha, Abbavaram Gowtham Reddy, Abhinav Kumar, Saketh Bachu, Vineeth N. Balasubramanian, and Amit Sharma · 2024
Closest in time.