ITEM METADATA RECORD
Title: Learning causal Bayesian networks from observations and experiments: A decision theoretic approach
Authors: Meganck, Sieglinde ×
Leray, P
Manderick, B #
Issue Date: 2006
Publisher: Springer-verlag berlin
Series Title: Modeling decisions for artificial intelligence vol:3885 pages:58-69
Abstract: We discuss a decision theoretic approach to learn causal Bayesian networks from observational data and experiments. We use the information of observational data to learn a completed partially directed acyclic graph using a structure learning technique and try to discover the directions of the remaining edges by means of experiment. We will show that our approach allows to learn a causal Bayesian network optimally with relation to a number of decision criteria. Our method allows the possibility to assign costs to each experiment and each measurement. We introduce an algorithm that allows to actively add results of experiments so that arcs can be directed during learning. A numerical example is given as demonstration of the techniques.
ISSN: 0302-9743
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Research Group on Psychotherapy and Psychodynamic psychology (-)
× corresponding author
# (joint) last author

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