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In this paper, we discuss structure learning of causal networks from multiple data sets obtained by external intervention experiments where we do not know what variables are manipulated.
Probabilistic reasoning in intelligent systems: networks of plausible inference
J. Pearl and G. Shafer · 1988
Earlier work this paper cites.
A Case Study with two Probabilistic Inference Techniques for Belief Networks
I.A. Beinlich, M. Suermondt, R.M. Chavez, and G.F. Cooper · 1989
Earlier work this paper cites.
Equivalence and synthesis of causal models
T. Verma and J. Pearl · 1990
Earlier work this paper cites.
Causal diagrams for empirical research
J. Pearl · 1995
Earlier work this paper cites.
Learning Bayesian networks with discrete variables from data
P. Spirtes and C. Meek · 1995
Earlier work this paper cites.
Causal discovery from a mixture of experimental and observational data
G. Cooper and C. Yoo · 1999
Earlier work this paper cites.
A Bayesian approach to causal discovery
D. Heckerman, C. Meek, and G. Cooper · 1999
Earlier work this paper cites.
Causality: Models, reasoning, and inference
J. Pearl · 2000
Earlier work this paper cites.
Causation, prediction, and search
P. Spirtes, C.N. Glymour, and R. Scheines · 2001
Earlier work this paper cites.
Learning the Causal Structure of Overlapping Variable Sets
D. Danks · 2002
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Estimating high-dimensional intervention effects from observational data
M. H. Maathuis, M. Kalisch, and P. Bühlmann · 2009
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Integrating locally learned causal structures with overlapping variables
R. E. Tillman, D. Danks, and C. Glymour · 2009
Later among the works it cites.
Robust graphical modeling of gene networks using classical and alternative t-distributions
M. Finegold and M. Drton · 2011
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Characterization and greedy learning of interventional markov equivalence classes of directed acyclic graphs
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F. Eberhardt, C. Glymour, and R. Scheines · 2005
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F. Eberhardt · 2006
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D. Eaton and K. Murphy · 2007
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Interventions and causal inference
F. Eberhardt and R. Scheines · 2007
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Learning from mixture of experimental data: a constraint–based approach
L. Vincenzo, T. Ioannis, and T. Sofia · 2012
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Constraint-based causal discovery from multiple interventions over overlapping variable sets
S. Triantafillou and I. Tsamardinos · 2014
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