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We consider recovering a causal graph in presence of latent variables, where we seek to minimize the cost of interventions used in the recovery process.
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An algorithm for deciding if a set of observed independencies has a causal explanation
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Causality: models, reasoning and inference , volume 29
Judea Pearl · 2000
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Peter Spirtes, Clark N Glymour, Richard Scheines, David Heckerman, Christopher Meek, Gregory Cooper, and Thomas Richardson · 2000
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Ancestral graph markov models
Thomas Richardson, Peter Spirtes, et al · 2002
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Flattening antichains
Ákos Kisvölcsey · 2006
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A linear non-gaussian acyclic model for causal discovery
Shohei Shimizu, Patrik O Hoyer, Aapo Hyvärinen, and Antti Kerminen · 2006
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Learning the structure of linear latent variable models
Ricardo Silva, Richard Scheine, Clark Glymour, and Peter Spirtes · 2006
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Causation and intervention
Frederick Eberhardt · 2007
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Frederick Eberhardt and Richard Scheines · 2007
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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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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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Local characterizations of causal bayesian networks
Elias Bareinboim, Carlos Brito, and Judea Pearl · 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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Optimal algorithms for testing closeness of discrete distributions
Siu-On Chan, Ilias Diakonikolas, Paul Valiant, and Gregory Valiant · 2014
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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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High-dimensional learning of linear causal networks via inverse covariance estimation
Po-Ling Loh and Peter Bühlmann · 2014
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Learning causal graphs with small interventions
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Causality: Models, Reasoning, and Inference
Judea Pearl · 2009
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Extremal combinatorics: with applications in computer science
Stasys Jukna · 2011
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Ancestor relations in the presence of unobserved variables
Pekka Parviainen and Mikko Koivisto · 2011
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Kernel-based conditional independence test and application in causal discovery
Kun Zhang, Jonas Peters, Dominik Janzing, and Bernhard Schölkopf · 2011
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Experiment selection for causal discovery
Antti Hyttinen, Frederick Eberhardt, and Patrik O Hoyer
Cited in the paper.
Discovering cyclic causal models with latent variables: A general sat-based procedure
Antti Hyttinen, Patrik O Hoyer, Frederick Eberhardt, and Matti Jarvisalo
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Karthikeyan Shanmugam, Murat Kocaoglu, Alexandros G Dimakis, and Sriram Vishwanath · 2015
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Causal inference and the data-fusion problem
Elias Bareinboim and Judea Pearl · 2016
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Testing conditional independence of discrete distributions
Clément L Canonne, Ilias Diakonikolas, Daniel M Kane, and Alistair Stewart · 2018
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Causal structure learning
Christina Heinze-Deml, Marloes H Maathuis, and Nicolai Meinshausen · 2018
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Experimental design for cost-aware learning of causal graphs
Erik Lindgren, Murat Kocaoglu, Alexandros G Dimakis, and Sriram Vishwanath · 2018
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