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Causal models can compactly and efficiently encode the data-generating process under all interventions and hence may generalize better under changes in distribution.
Learning bayesian networks: The combination of knowledge and statistical data
David Heckerman, Dan Geiger, and David M Chickering · 1995
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Causal Discovery from a Mixture of Experimental and Observational Data
Gregory F. Cooper and Changwon Yoo · 1999
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Causation, prediction, and search
Peter Spirtes, Clark N Glymour, Richard Scheines, David Heckerman, Christopher Meek, Gregory Cooper, and Thomas Richardson · 2000
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Optimal structure identification with greedy search
David Maxwell Chickering · 2002
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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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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
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Rectified linear units improve restricted boltzmann machines
Vinod Nair and Geoffrey E Hinton · 2010
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Identifiability of causal graphs using functional models
J. Peters, J. M. Mooij, D. Janzing, and B. Schölkopf · 2011
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Frederick Eberhardt, Clark Glymour, and Richard Scheines · 2012
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Characterization and greedy learning of interventional markov equivalence classes of directed acyclic graphs
Alain Hauser and Peter Bühlmann · 2012
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A meta-transfer objective for learning to disentangle causal mechanisms
Yoshua Bengio, Tristan Deleu, Nasim Rahaman, Rosemary Ke, Sébastien Lachapelle, Olexa Bilaniuk, Anirudh Goyal, and Christopher Pal · 2019
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Causal reasoning from meta-reinforcement learning
Ishita Dasgupta, Jane Wang, Silvia Chiappa, Jovana Mitrovic, Pedro Ortega, David Raposo, Edward Hughes, Peter Battaglia, Matthew Botvinick, and Zeb Kurth-Nelson · 2019
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Causal confusion in imitation learning
Pim de Haan, Dinesh Jayaraman, and Sergey Levine · 2019
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Recurrent independent mechanisms
Anirudh Goyal, Alex Lamb, Jordan Hoffmann, Shagun Sodhani, Sergey Levine, Yoshua Bengio, and Bernhard Schölkopf · 2019
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Shaping belief states with generative environment models for rl
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Diederik P Kingma and Jimmy Ba · 2014
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Causal generative neural networks
Olivier Goudet, Diviyan Kalainathan, Philippe Caillou, Isabelle Guyon, David Lopez-Paz, and Michèle Sebag · 2017
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Causal structure learning
Christina Heinze-Deml, Marloes H Maathuis, and Nicolai Meinshausen · 2018
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Karol Gregor, Danilo Jimenez Rezende, Frederic Besse, Yan Wu, Hamza Merzic, and Aaron van den Oord · 2019
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Learning neural causal models from unknown interventions
Nan Rosemary Ke, Olexa Bilaniuk, Anirudh Goyal, Stefan Bauer, Hugo Larochelle, Chris Pal, and Yoshua Bengio · 2019
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Causal induction from visual observations for goal directed tasks
Suraj Nair, Yuke Zhu, Silvio Savarese, and Li Fei-Fei · 2019
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Causality for machine learning
Bernhard Schölkopf · 2019
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