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The fundamental challenge in causal induction is to infer the underlying graph structure given observational and/or interventional data.
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N-1 experiments suffice to determine the causal relations among n variables
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Daphne Koller and Nir Friedman · 2009
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Povilas Daniusis, Dominik Janzing, Joris Mooij, Jakob Zscheischler, Bastian Steudel, Kun Zhang, and Bernhard Schölkopf · 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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Timo Koski and John Noble · 2012
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Kernel-based conditional independence test and application in causal discovery
Kun Zhang, Jonas Peters, Dominik Janzing, and Bernhard Schölkopf · 2012
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Cause-effect pairs kaggle competition, 2013
Isabelle Guyon · 2013
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Chalearn fast causation coefficient challenge, 2014
Isabelle Guyon · 2014
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Diederik P Kingma and Jimmy Ba · 2014
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Ahmed Mabrouk, Christophe Gonzales, Karine Jabet-Chevalier, and Eric Chojnacki · 2014
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Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Joint causal inference from multiple contexts
Joris M Mooij, Sara Magliacane, and Tom Claassen · 2016
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Sébastien Lachapelle, Philippe Brouillard, Tristan Deleu, and Simon Lacoste-Julien · 2019
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Yang Li, Shoaib Akbar, and Junier B Oliva · 2019
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Dag-gnn: Dag structure learning with graph neural networks
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Shengyu Zhu and Zhitang Chen · 2019
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Transformers can do bayesian inference
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