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We consider structural equation models in which variables can be written as a function of their parents and noise terms, which are assumed to be jointly independent.
A transformational characterization of equivalent Bayesian network structures
D. M. Chickering · 1995
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Spectra of Graphs: Theory and Application
D.M. Cvetković, M. Doob, and H. Sachs · 1995
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Graphical Models
S. Lauritzen · 1996
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A characterization of Markov equivalence classes for acyclic digraphs
S. A. Andersson, D. Madigan, and M. D. Perlman · 1997
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Causation, Prediction, and Search
P. Spirtes, C. Glymour, and R. Scheines · 2000
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Optimal structure identification with greedy search
D. M. Chickering · 2002
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Boosting for tumor classification with gene expression data
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Detection of unfaithfulness and robust causal inference
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Causality: Models, Reasoning, and Inference
J. Pearl · 2009
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Nonlinear directed acyclic structure learning with weakly additive noise models
R. Tillman, A. Gretton, and P. Spirtes · 2010
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The on-line encyclopedia of integer sequences
OEIS Foundation Inc · 2011
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Identifiability of causal graphs using functional models
J. Peters, J. M. Mooij, D. Janzing, and B. Schölkopf · 2011
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High-dimensional statistics with a view towards applications in biology
P. Bühlmann, M. Kalisch, and L. Meier · 2013
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Geometry of the faithfulness assumption in causal inference
C. Uhler, G. Raskutti, P. Bühlmann, and B. Yu · 2013
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ℓ 0 \ell_{0} -penalized maximum likelihood for sparse directed acyclic graphs
S. van de Geer and P. Bühlmann · 2013
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