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We study the problem of causal structure learning over a set of random variables when the experimenter is allowed to perform at most $M$ experiments in a non-adaptive manner.
Equivalence and synthesis of causal models
Pearl, TS Verma Judea · 1991
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An algorithm for fast recovery of sparse causal graphs
Spirtes, Peter and Glymour, Clark · 1991
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Emergence of scaling in random networks
Barabási, Albert-László and Albert, Réka · 1999
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Causal discovery from a mixture of experimental and observational data
Cooper, Gregory F and Yoo, Changwon · 1999
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Causal diagrams for epidemiologic research
Greenland, Sander, Pearl, Judea, and Robins, James M · 1999
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Causation, prediction, and search
Spirtes, Peter, Glymour, Clark N, and Scheines, Richard · 2000
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On the number of experiments sufficient and in the worst case necessary to identify all causal relations among n variables
Eberhardt, Frederick, Glymour, Clark, and Scheines, Richard · 2005
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Making things happen: A theory of causal explanation
Woodward, James · 2005
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Regulondb (version 5.0): Escherichia coli k-12 transcriptional regulatory network, operon organization, and growth conditions
Salgado, Heladia, Gama-Castro, Socorro, Peralta-Gil, Martin, Díaz-Peredo, Edgar, Sánchez-Solano, Fabiola, Santos-Zavaleta, Alberto, Martínez-Flores, Irma, Jiménez-Jacinto, Verónica, Bonavides-Martínez, César, Segura-Salazar, Juan, et al · 2006
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A linear non-gaussian acyclic model for causal discovery
Shimizu, Shohei, Hoyer, Patrik O, Hyvärinen, Aapo, and Kerminen, Antti · 2006
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Eberhardt, Frederick · 2007
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Active learning of causal networks with intervention experiments and optimal designs
He, Yang-Bo and Geng, Zhi · 2008
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Nonlinear causal discovery with additive noise models
Hoyer, Patrik O, Janzing, Dominik, Mooij, Joris M, Peters, Jonas, and Schölkopf, Bernhard · 2009
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Identifiability of gaussian structural equation models with equal error variances
Peters, Jonas and Bühlmann, Peter · 2012
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Experiment selection for causal discovery
Hyttinen, Antti, Eberhardt, Frederick, and Hoyer, Patrik O · 2013
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Two optimal strategies for active learning of causal models from interventional data
Hauser, Alain and Bühlmann, Peter · 2014
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Learning causal graphs with small interventions
Shanmugam, Karthikeyan, Kocaoglu, Murat, Dimakis, Alexandros G, and Vishwanath, Sriram · 2015
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Network science
Barabási, Albert-László · 2016
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Methods for causal inference from gene perturbation experiments and validation
Meinshausen, Nicolai, Hauser, Alain, Mooij, Joris M, Peters, Jonas, Versteeg, Philip, and Bühlmann, Peter · 2016
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Koller, Daphne and Friedman, Nir · 2009
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Causality
Pearl, Judea · 2009
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Learning gene regulatory networks from only positive and unlabeled data
Cerulo, Luigi, Elkan, Charles, and Ceccarelli, Michele · 2010
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Distinguishing cause from effect using observational data: methods and benchmarks
Mooij, Joris M, Peters, Jonas, Janzing, Dominik, Zscheischler, Jakob, and Schölkopf, Bernhard · 2016
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Causal inference by using invariant prediction: identification and confidence intervals
Peters, Jonas, Bühlmann, Peter, and Meinshausen, Nicolai · 2016
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