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A new causal discovery method, Structural Agnostic Modeling (SAM), is presented in this paper.
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Ioannis Tsamardinos, Laura E Brown, and Constantin F Aliferis · 2006
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Arthur Gretton, Karsten M Borgwardt, Malte Rasch, Bernhard Schölkopf, Alexander J Smola, et al · 2007
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Markus Kalisch and Peter Bühlmann · 2007
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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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The dream4 in-silico network challenge
Daniel Marbach, Thomas Schaffter, Dario Floreano, Robert J Prill, and Gustavo Stolovitzky · 2009
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Causality
Judea Pearl · 2009
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Marco Scutari · 2009
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Local causal and Markov blanket induction for causal discovery and feature selection for classification part I: Algorithms and empirical evaluation
Constantin F Aliferis, Alexander Statnikov, Ioannis Tsamardinos, Subramani Mani, and Xenofon D Koutsoukos · 2010
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Inferring regulatory networks from expression data using tree-based methods
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Dominik Janzing and Bernhard Scholkopf · 2010
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Joris M Mooij, Oliver Stegle, Dominik Janzing, Kun Zhang, , and Bernhard Schölkopf · 2010
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Estimating divergence functionals and the likelihood ratio by convex risk minimization
XuanLong Nguyen, Martin J Wainwright, and Michael I Jordan · 2010
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Kun Zhang and Aapo Hyvärinen · 2010
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Scikit-learn: Machine learning in Python
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David Lopez-Paz, Krikamol Muandet, Bernhard Schölkopf, and Ilya O Tolstikhin · 2015
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High-dimensional consistency in score-based and hybrid structure learning
Preetam Nandy, Alain Hauser, and Marloes H Maathuis · 2015
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Scaling up greedy causal search for continuous variables
Joseph D Ramsey · 2015
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Backshift: Learning causal cyclic graphs from unknown shift interventions
Dominik Rothenhäusler, Christina Heinze, Jonas Peters, and Nicolai Meinshausen · 2015
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Learning large-scale bayesian networks with the sparsebn package
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Progressive growing of GANs for improved quality, stability, and variation
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Learning sparse neural networks through l _ 0 l\_0 regularization
Christos Louizos, Max Welling, and Diederik P Kingma · 2017
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Exploring generalization in deep learning
Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, and Nati Srebro · 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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Joseph Ramsey, Madelyn Glymour, Ruben Sanchez-Romero, and Clark Glymour · 2017
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Approximate kernel-based conditional independence tests for fast non-parametric causal discovery
Eric V Strobl, Kun Zhang, and Shyam Visweswaran · 2017
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Cause-effect inference by comparing regression errors
Patrick Blöbaum, Dominik Janzing, Takashi Washio, Shohei Shimizu, and Bernhard Schölkopf · 2018
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