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ERIC Number: ED518353
Record Type: Non-Journal
Publication Date: 2010
Pages: 113
Abstractor: As Provided
Reference Count: 0
ISBN: ISBN-978-1-1241-4727-7
Causal Discovery of Dynamic Systems
Voortman, Mark
ProQuest LLC, Ph.D. Dissertation, University of Pittsburgh
Recently, several philosophical and computational approaches to causality have used an interventionist framework to clarify the concept of causality [Spirtes et al., 2000, Pearl, 2000, Woodward, 2005]. The characteristic feature of the interventionist approach is that causal models are potentially useful in predicting the effects of manipulations. One of the main motivations of such an undertaking comes from humans, who seem to create sophisticated mental causal models that they use to achieve their goals by manipulating the world. Several algorithms have been developed to learn static causal models from data that can be used to predict the effects of interventions [e.g., Spirtes et al., 2000]. However, Dash [2003, 2005] argued that when such equilibrium models do not satisfy what he calls the "Equilibration-Manipulation Commutability (EMC)" condition, causal reasoning with these models will be incorrect, making dynamic models indispensable. It is shown that existing approaches to learning dynamic models [e.g., Granger, 1969, Swanson and Granger, 1997] are unsatisfactory, because they do not perform a necessary search for hidden variables. The main contribution of this dissertation is, to the best of my knowledge, the first provably correct learning algorithm that discovers dynamic causal models from data, which can then be used for causal reasoning even if the EMC condition is violated. The representation that is used for dynamic causal models is called "Difference-Based Causal Models (DBCMs)" and is based on Iwasaki and Simon [1994]. A comparison will be made to other approaches and the algorithm, called DBCM Learner, is empirically tested by learning physical systems from artificially generated data. The approach is also used to gain insights into the intricate workings of the brain by learning DBCMs from EEG data and MEG data. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page:]
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Publication Type: Dissertations/Theses - Doctoral Dissertations
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A