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.
| Identifer | oai:union.ndltd.org:PITT/oai:PITTETD:etd-01212010-164420 |
| Date | 25 January 2010 |
| Creators | Voortman, Mark Johannes |
| Contributors | Clark Glymour, Stephen Hirtle, Roger Flynn, Marek J. Druzdzel, Denver Dash |
| Publisher | University of Pittsburgh |
| Source Sets | University of Pittsburgh |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.library.pitt.edu/ETD/available/etd-01212010-164420/ |
| Rights | unrestricted, I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to University of Pittsburgh or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. |
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