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Causal structure learning has been a challenging task in the past decades and several mainstream approaches such as constraint- and score-based methods have been studied with theoretical guarantees.
Probabilistic network construction using the minimum description length principle
Remco R Bouckaert · 1993
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.
Causal inference and causal explanation with background knowledge
Christopher Meek · 1995
Earlier work this paper cites.
Efficient approximations for the marginal likelihood of bayesian networks with hidden variables
David M Chickering and David Heckerman · 1997
Earlier work this paper cites.
Nonlinear Programming
Dimitri P Bertsekas · 1999
Earlier work this paper cites.
Causation, prediction, and search
Peter Spirtes, Clark N Glymour, Richard Scheines, David Heckerman, Christopher Meek, Gregory Cooper, and Thomas Richardson · 2000
Earlier work this paper cites.
Optimal structure identification with greedy search
David M Chickering · 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, and Antti Kerminen · 2006
Earlier work this paper cites.
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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Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
Daphne Koller and Nir Friedman · 2009
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Causality: Models, Reasoning and Inference
Judea Pearl · 2009
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On the identifiability of the post-nonlinear causal model
Kun Zhang and Aapo Hyvärinen · 2009
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Causal inference using the algorithmic Markov condition
Dominik Janzing and Bernhard Schölkopf · 2010
Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
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Elements of Causal Inference: Foundations and Learning Algorithms
Jonas Peters, Dominik Janzing, and Bernhard Schölkopf · 2017
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Scalable probabilistic causal structure discovery
Dhanya Sridhar, Jay Pujara, and Lise Getoor · 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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Demystifying parallel and distributed deep learning: An in-depth concurrency analysis
Tal Ben-Nun and Torsten Hoefler · 2019
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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.
Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2014
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Keting Cen, Huawei Shen, Jinhua Gao, Qi Cao, Bingbing Xu, and Xueqi Cheng · 2019
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The seven tools of causal inference, with reflections on machine learning
Judea Pearl · 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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