Fetching the paper…
Reading the bibliography…
Causal discovery aims to uncover causal structure among a set of variables.
Learning gaussian networks
Dan Geiger and David Heckerman · 1994
Earlier work this paper cites.
Learning bayesian belief networks: An approach based on the mdl principle
Wai Lam and Fahiem Bacchus · 1994
Earlier work this paper cites.
Learning bayesian networks: The combination of knowledge and statistical data
David Heckerman, Dan Geiger, and David M Chickering · 1995
Earlier work this paper cites.
Learning bayesian networks is np-complete
David Maxwell Chickering · 1996
Earlier work this paper cites.
Are there algorithms that discover causal structure?
David Freedman and Paul Humphreys · 1999
Earlier work this paper cites.
Causation, prediction, and search
Peter Spirtes, Clark N Glymour, Richard Scheines, and David Heckerman · 2000
Earlier work this paper cites.
Optimal structure identification with greedy search
David Maxwell Chickering · 2002
Earlier work this paper cites.
Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic bayesian networks
Dirk Husmeier · 2003
Earlier work this paper cites.
Causal protein-signaling networks derived from multiparameter single-cell data
Karen Sachs, Omar Perez, Dana Pe’er, Douglas A Lauffenburger, and Garry P Nolan · 2005
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.
Estimating high-dimensional directed acyclic graphs with the pc-algorithm
Markus Kalisch and Peter Bühlman · 2007
Earlier work this paper cites.
Beware of the dag!
A Philip Dawid · 2010
Earlier work this paper cites.
Ordering-based search: A simple and effective algorithm for learning bayesian networks
Marc Teyssier and Daphne Koller · 2012
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
Earlier work this paper cites.
Causal discovery with continuous additive noise models
Jonas Peters, Joris M Mooij, Dominik Janzing, and Bernhard Schölkopf · 2014
Cited alongside, same era.
Cam: Causal additive models, high-dimensional order search and penalized regression
Peter Bühlmann, Jonas Peters, and Jan Ernest · 2014
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
Cited alongside, same era.
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.
Elements of causal inference: foundations and learning algorithms
Jonas Peters, Dominik Janzing, and Bernhard Schölkopf · 2017
Cited alongside, same era.
Causal adversarial network for learning conditional and interventional distributions
Raha Moraffah, Bahman Moraffah, Mansooreh Karami, Adrienne Raglin, and Huan Liu · 2020
Later among the works it cites.
Castle: Regularization via auxiliary causal graph discovery
Trent Kyono, Yao Zhang, and Mihaela van der Schaar · 2020
Later among the works it cites.
On the role of sparsity and dag constraints for learning linear dags
Ignavier Ng, AmirEmad Ghassami, and Kun Zhang · 2020
Later among the works it cites.
Ordering-based causal discovery with reinforcement learning
Xiaoqiang Wang, Yali Du, Shengyu Zhu, Liangjun Ke, Zhitang Chen, Jianye Hao, and Jun Wang · 2021
Later among the works it cites.
Causalvae: Disentangled representation learning via neural structural causal models
Mengyue Yang, Furui Liu, Zhitang Chen, Xinwei Shen, Jianye Hao, and Jun Wang · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Xun Zheng, Bryon Aragam, Pradeep K Ravikumar, and Eric P Xing · 2018
Cited alongside, same era.
The book of why: the new science of cause and effect
Judea Pearl and Dana Mackenzie · 2018
Cited alongside, same era.
Structural agnostic modeling: Adversarial learning of causal graphs
Diviyan Kalainathan, Olivier Goudet, Isabelle Guyon, David Lopez-Paz, and Michèle Sebag · 2018
Cited alongside, same era.
Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
Cited alongside, same era.
A constraint-based algorithm for causal discovery with cycles, latent variables and selection bias
Eric V Strobl · 2019
Cited alongside, same era.
Gradient-based neural dag learning
Sébastien Lachapelle, Philippe Brouillard, Tristan Deleu, and Simon Lacoste-Julien · 2019
Cited alongside, same era.
Causal discovery with reinforcement learning
Shengyu Zhu, Ignavier Ng, and Zhitang Chen · 2019
Cited alongside, same era.
Flow network based generative models for non-iterative diverse candidate generation, 2021
Emmanuel Bengio, Moksh Jain, Maksym Korablyov, Doina Precup, and Yoshua Bengio · 2021
Later among the works it cites.
Gflownet foundations, 2021
Yoshua Bengio, Tristan Deleu, Edward J. Hu, Salem Lahlou, Mo Tiwari, and Emmanuel Bengio · 2021
Later among the works it cites.
Beware of the simulated dag! causal discovery benchmarks may be easy to game
Alexander Reisach, Christof Seiler, and Sebastian Weichwald · 2021
Later among the works it cites.
Bayesian structure learning with generative flow networks
Tristan Deleu, António Góis, Chris Emezue, Mansi Rankawat, Simon Lacoste-Julien, Stefan Bauer, and Yoshua Bengio · 2022
Closest in time.
Reinforcement causal structure learning on order graph
Dezhi Yang, Guoxian Yu, Jun Wang, Zhengtian Wu, and Maozu Guo · 2022
Closest in time.
Generative flow networks for discrete probabilistic modeling
Dinghuai Zhang, Nikolay Malkin, Zhen Liu, Alexandra Volokhova, Aaron Courville, and Yoshua Bengio · 2022
Closest in time.
Biological sequence design with gflownets
Moksh Jain, Emmanuel Bengio, Alex Hernandez-Garcia, Jarrid Rector-Brooks, Bonaventure FP Dossou, Chanakya Ajit Ekbote, Jie Fu, Tianyu Zhang, Michael Kilgour, Dinghuai Zhang, et al · 2022
Closest in time.
Trajectory balance: Improved credit assignment in gflownets
Nikolay Malkin, Moksh Jain, Emmanuel Bengio, Chen Sun, and Yoshua Bengio · 2022
Closest in time.