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We present Causal Generative Neural Networks (CGNNs) to learn functional causal models from observational data.
Approximation by superpositions of a sigmoidal function
George Cybenko · 1989
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Causation, prediction, and search
Peter Spirtes, Clark N Glymour, and Richard Scheines · 2000
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Optimal structure identification with greedy search
David Maxwell Chickering · 2002
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Causality: models, reasoning and inference
Judea Pearl · 2003
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Kernel methods for measuring independence
Arthur Gretton, Ralf Herbrich, Alexander Smola, Olivier Bousquet, and Bernhard Schölkopf · 2005
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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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A kernel method for the two-sample-problem
Arthur Gretton, Karsten M Borgwardt, Malte Rasch, Bernhard Schölkopf, Alexander J Smola, et al · 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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Causality
Judea Pearl · 2009
Earlier work this paper cites.
On the identifiability of the post-nonlinear causal model
Kun Zhang and Aapo Hyvärinen · 2009
Cited alongside, same era.
Probabilistic latent variable models for distinguishing between cause and effect
Oliver Stegle, Dominik Janzing, Kun Zhang, Joris M Mooij, and Bernhard Schölkopf · 2010
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Learning high-dimensional directed acyclic graphs with latent and selection variables
Diego Colombo, Marloes H Maathuis, Markus Kalisch, and Thomas S Richardson · 2012
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Inferring deterministic causal relations
Povilas Daniusis, Dominik Janzing, Joris Mooij, Jakob Zscheischler, Bastian Steudel, Kun Zhang, and Bernhard Schölkopf · 2012
Cited alongside, same era.
Causal inference using graphical models with the r package pcalg
Markus Kalisch, Martin Mächler, Diego Colombo, Marloes H Maathuis, Peter Bühlmann, et al · 2012
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Chalearn cause effect pairs challenge, 2013
Generative moment matching networks
Yujia Li, Kevin Swersky, and Richard S Zemel · 2015
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Towards a learning theory of cause-effect inference
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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Vector quantile regression beyond correct specification
Guillaume Carlier, Victor Chernozhukov, and Alfred Galichon · 2016
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Conditional distribution variability measures for causality detection
José Fonollosa · 2016
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Deep Learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
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Isabelle Guyon · 2013
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Structural intervention distance (sid) for evaluating causal graphs
Jonas Peters and Peter Bühlmann · 2013
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Order-independent constraint-based causal structure learning
Diego Colombo and Marloes H Maathuis · 2014
Cited alongside, same era.
Adam: A Method for Stochastic Optimization
Durk P Kingma and Jimmy Ba · 2014
Cited alongside, same era.
Causal discovery with continuous additive noise models
Jonas Peters, Joris M Mooij, Dominik Janzing, and Bernhard Schölkopf · 2014
Cited alongside, same era.
High-dimensional feature selection by feature-wise kernelized lasso
Makoto Yamada, Wittawat Jitkrittum, Leonid Sigal, Eric P Xing, and Masashi Sugiyama · 2014
Cited alongside, same era.
From dependence to causation
David Lopez-Paz · 2016
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Distinguishing cause from effect using observational data: methods and benchmarks
Joris M Mooij, Jonas Peters, Dominik Janzing, Jakob Zscheischler, and Bernhard Schölkopf · 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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ConvNets and ImageNet Beyond Accuracy: Explanations, Bias Detection, Adversarial Examples and Model Criticism
P. Stock and M. Cisse · 2017
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