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We study the problem of causal discovery through targeted interventions.
A mathematical theory of communication
Claude Elwood Shannon · 1948
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A bayesian approach to learning causal networks
David Heckerman · 1995
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Kathryn Chaloner and Isabella Verdinelli · 1995
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Alison Gopnik · 1996
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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, and Richard Scheines · 2000
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Gaussian process networks
Nir Friedman and Iftach Nachman · 2000
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Causal discovery from changes
Jin Tian and Judea Pearl · 2001
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Active learning of causal bayes net structure
Kevin P Murphy · 2001
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Active learning for structure in bayesian networks
Simon Tong and Daphne Koller · 2001
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Being bayesian about network structure. a bayesian approach to structure discovery in bayesian networks
Nir Friedman and Daphne Koller · 2003
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Exact bayesian structure discovery in bayesian networks
Mikko Koivisto and Kismat Sood · 2004
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A bayesian approach to causal discovery
David Heckerman, Christopher Meek, and Gregory Cooper · 2006
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Gaussian processes for machine learning , volume 2
Christopher KI Williams and Carl Edward Rasmussen · 2006
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N-1 experiments suffice to determine the causal relations among n variables
Frederick Eberhardt, Clark Glymour, and Richard Scheines · 2006
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Bayesian approach to global optimization: theory and applications , volume 37
Jonas Mockus · 2012
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Information-geometric approach to inferring causal directions
Dominik Janzing, Joris Mooij, Kun Zhang, Jan Lemeire, Jakob Zscheischler, Povilas Daniušis, Bastian Steudel, and Bernhard Schölkopf · 2012
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Two optimal strategies for active learning of causal models from interventional data
Alain Hauser and Peter Bühlmann · 2014
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Causal discovery with continuous additive noise models
Jonas Peters, Joris M Mooij, Dominik Janzing, and Bernhard Schölkopf · 2014
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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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Reconstructing causal biological networks through active learning
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Variational inference for dirichlet process mixtures
David M Blei, Michael I Jordan, et al · 2006
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Exact Bayesian structure learning from uncertain interventions
Daniel Eaton and Kevin Murphy · 2007
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Almost optimal intervention sets for causal discovery
Frederick Eberhardt · 2008
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Active learning of causal networks with intervention experiments and optimal designs
Yang-Bo He and Zhi Geng · 2008
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Causality
Judea Pearl · 2009
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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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Hyunghoon Cho, Bonnie Berger, and Jian Peng · 2016
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Building machines that learn and think like people
Brenden M Lake, Tomer D Ullman, Joshua B Tenenbaum, and Samuel J Gershman · 2017
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A bayesian active learning experimental design for inferring signaling networks
Robert Osazuwa Ness, Karen Sachs, Parag Mallick, and Olga Vitek · 2017
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Budgeted experiment design for causal structure learning
AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, and Elias Bareinboim · 2017
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Probabilistic active learning of functions in structural causal models
Paul K Rubenstein, Ilya Tolstikhin, Philipp Hennig, and Bernhard Schölkopf · 2017
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Minimal i-map mcmc for scalable structure discovery in causal dag models
Raj Agrawal, Caroline Uhler, and Tamara Broderick · 2018
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Abcd-strategy: Budgeted experimental design for targeted causal structure discovery
Raj Agrawal, Chandler Squires, Karren Yang, Karthik Shanmugam, and Caroline Uhler · 2019
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