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We consider the problem of learning causal networks with interventions, when each intervention is limited in size under Pearl's Structural Equation Model with independent errors (SEM-IE).
On separating systems of a finite set
Gyula Katona · 1966
Earlier work this paper cites.
A separator theorem for planar graphs
Richard J Lipton and Robert Endre Tarjan · 1979
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On separating systems whose elements are sets of at most k elements
Ingo Wegener · 1979
Earlier work this paper cites.
An algorithm for deciding if a set of observed independencies has a causal explanation
Thomas Verma and Judea Pearl · 1992
Earlier work this paper cites.
Causal inference and causal explanation with background knowledge
Christopher Meek · 1995
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Strong completeness and faithfulness in bayesian networks
Christopher Meek · 1995
Earlier work this paper cites.
A characterization of markov equivalence classes for acyclic digraphs
Steen A. Andersson, David Madigan, and Michael D. Perlman · 1997
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Causation, Prediction, and Search
Peter Spirtes, Clark Glymour, and Richard Scheines · 2001
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A linear non-gaussian acyclic model for causal discovery
S Shimizu, P. O Hoyer, A Hyvarinen, and A. J Kerminen · 2006
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Causation and Intervention (Ph.D. Thesis)
Frederick Eberhardt · 2007
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Nonlinear causal discovery with additive noise models
Patrik O Hoyer, Dominik Janzing, Joris Mooij, Jonas Peters, and Bernhard Schölkopf · 2008
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Causality: Models, Reasoning and Inference
Judea Pearl · 2009
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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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Two optimal strategies for active learning of causal networks from interventional data
Alain Hauser and Peter Bühlmann · 2012
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Experiment selection for causal discovery
Antti Hyttinen, Frederick Eberhardt, and Patrik Hoyer · 2013
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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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Randomized experimental design for causal graph discovery
Huining Hu, Zhentao Li, and Adrian Vetta · 2014
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On the number of experiments sufficient and in the worst case necessary to identify all causal relations among n variables
Frederich Eberhardt, Clark Glymour, and Richard Scheines
Cited in the paper.