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Causal Discovery of Dynamic Systems

Voortman, Mark Johannes (2010) Causal Discovery of Dynamic Systems. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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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.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Voortman, Mark
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee Chairzdzel, Marek J.marek@sis.pitt.eduDRUZDZEL
Committee MemberGlymour,
Committee MemberDash,
Committee MemberFlynn,
Committee MemberHirtle, Stephenhirtle@pitt.eduHIRTLE
Date: 25 January 2010
Date Type: Completion
Defense Date: 3 December 2009
Approval Date: 25 January 2010
Submission Date: 21 January 2010
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Institution: University of Pittsburgh
Schools and Programs: School of Information Sciences > Information Science
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Causal discovery; dynamic systems; time series
Other ID:, etd-01212010-164420
Date Deposited: 10 Nov 2011 19:31
Last Modified: 15 Nov 2016 13:36


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