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Discovering causal structure among a set of variables is a fundamental problem in many domains.
Estimating the dimension of a model
Gideon Schwarz et al · 1978
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Akaike information criterion statistics
Yosiyuki Sakamoto, Makio Ishiguro, and Genshiro Kitagawa · 1986
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Actor-critic algorithms
Vijay R Konda and John N Tsitsiklis · 2000
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Causation, prediction, and search
Peter Spirtes, Clark N Glymour, Richard Scheines, and David Heckerman · 2000
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Optimal structure identification with greedy search
David Maxwell Chickering · 2002
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Large-sample learning of bayesian networks is np-hard
David Maxwell Chickering, David Heckerman, and Christopher Meek · 2004
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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
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A linear non-gaussian acyclic model for causal discovery
Shohei Shimizu, Patrik O Hoyer, Aapo Hyvärinen, and Antti Kerminen · 2006
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The max-min hill-climbing bayesian network structure learning algorithm
Ioannis Tsamardinos, Laura E Brown, and Constantin F Aliferis · 2006
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On the completeness of orientation rules for causal discovery in the presence of latent confounders and selection bias
Jiji Zhang · 2008
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Nonlinear causal discovery with additive noise models
Patrik O Hoyer, Dominik Janzing, Joris M Mooij, Jonas Peters, and Bernhard Schölkopf · 2009
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Controlling the false discovery rate of the association/causality structure learned with the pc algorithm
Junning Li and Z Jane Wang · 2009
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Causality
Judea Pearl · 2009
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Properties of bayesian dirichlet scores to learn bayesian network structures
Cassio Polpo de Campos and Qiang Ji · 2010
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Causal inference, 2010
Miguel A Hernan and James M Robins · 2010
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An introduction to causal inference
Judea Pearl · 2010
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Causal structure learning and inference: a selective review
Markus Kalisch and Peter Bühlmann · 2014
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Generalized score functions for causal discovery
Biwei Huang, Kun Zhang, Yizhu Lin, Bernhard Schölkopf, and Clark Glymour · 2018
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Introduction to the special section on missing data
Julie Josse, Jerome P Reiter, et al · 2018
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Deeper insights into graph convolutional networks for semi-supervised learning
Qimai Li, Zhichao Han, and Xiao-Ming Wu · 2018
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High-dimensional consistency in score-based and hybrid structure learning
Preetam Nandy, Alain Hauser, Marloes H Maathuis, et al · 2018
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Gain: Missing data imputation using generative adversarial nets
Jinsung Yoon, James Jordon, and Mihaela Van Der Schaar · 2018
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Dags with no tears: Continuous optimization for structure learning
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Jonas Peters, Joris M Mooij, Dominik Janzing, and Bernhard Schölkopf · 2014
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Structural intervention distance for evaluating causal graphs
Jonas Peters and Peter Bühlmann · 2015
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Score-based vs constraint-based causal learning in the presence of confounders
Sofia Triantafillou and Ioannis Tsamardinos · 2016
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Elements of causal inference: foundations and learning algorithms
Jonas Peters, Dominik Janzing, and Bernhard Schölkopf · 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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Xun Zheng, Bryon Aragam, Pradeep K Ravikumar, and Eric P Xing · 2018
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Gradient-based neural dag learning
Sébastien Lachapelle, Philippe Brouillard, Tristan Deleu, and Simon Lacoste-Julien · 2019
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Dag-gnn: Dag structure learning with graph neural networks
Yue Yu, Jie Chen, Tian Gao, and Mo Yu · 2019
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Causal Inference: What If
MA Hernán and JM Robins · 2020
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Missdeepcausal: Causal inference from incomplete data using deep latent variable models
Imke Mayer, Julie Josse, Félix Raimundo, and Jean-Philippe Vert · 2020
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Causal discovery with reinforcement learning
Shengyu Zhu, Ignavier Ng, and Zhitang Chen · 2020
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