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Causal structure learning is a key problem in many domains.
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“Causality”
Judea Pearl · 2009
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Alain Hauser and Peter Bühlmann · 2012
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Marcel Wienöbst, Max Bannach and Maciej Liśkiewicz · 2012
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Antti Hyttinen, Frederick Eberhardt and Patrik Hoyer · 2013
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Andreas Krause and Daniel Golovin · 2014
“Learning and Testing Causal Models with Interventions”
Jayadev Acharya, Arnab Bhattacharyya, Constantinos Daskalakis and Saravanan Kandasamy · 2018
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Erik Lindgren, Murat Kocaoglu, Alexandros Dimakis and Sriram Vishwanath · 2018
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Karren Yang, Abigail Katcoff and Caroline Uhler · 2018
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Raj Agrawal et al · 2019
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Yangbo He, Jinzhu Jia and Bin Yu · 2015
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Karthikeyan Shanmugam, Murat Kocaoglu, Alexandros Dimakis and Sriram Vishwanath · 2015
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Atray Dixit et al · 2016
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“Cost-Optimal Learning of Causal Graphs”
Murat Kocaoglu, Alex Dimakis and Sriram Vishwanath · 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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AmirEmad Ghassami, Saber Salehkaleybar and Negar Kiyavash · 2019
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Hamed Hassani, Amin Karbasi, Aryan Mokhtari and Zebang Shen · 2019
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“LazyIter: A Fast Algorithm for Counting Markov Equivalent DAGs and Designing Experiments”
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“Active Invariant Causal Prediction: Experiment Selection through Stability”
Juan Gamella and Christina Heinze-Deml · 2020
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“Identifiability and experimental design in perturbation studies”
Torsten Gross and Nils Blüthgen · 2020
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Aryan Mokhtari, Hamed Hassani and Amin Karbasi · 2020
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“Joint causal inference from multiple contexts”
Joris. Mooij, Sara Magliacane and Tom Claassen · 2020
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“Permutation-based causal structure learning with unknown intervention targets”
Chandler Squires, Yuhao Wang and Caroline Uhler · 2020
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