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Causal LearningPsychology, Philosophy, and Computation$
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Alison Gopnik and Laura Schulz

Print publication date: 2007

Print ISBN-13: 9780195176803

Published to Oxford Scholarship Online: April 2010

DOI: 10.1093/acprof:oso/9780195176803.001.0001

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Learning the Structure of Deterministic Systems

Learning the Structure of Deterministic Systems

Chapter:
(p.231) 14 Learning the Structure of Deterministic Systems
Source:
Causal Learning
Author(s):

Clark Glymour

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780195176803.003.0015

Consider a system represented by a directed acyclic graph with variables as vertices in which represented variable is a deterministic function of its parents. Most engineered systems without feedback instantiate such structures, and so, at least to appearances, do many macroscopic, natural inanimate systems. Learning the graphical representation of causal structure without experimental controls is especially difficult for such systems, because while the Markov condition holds, faithfulness does not. This chapter illustrates the problem and describes a heuristic (and not very satisfactory) learning procedure.

Keywords:   determinism, learning, causality, Markov, faithfulness

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