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dc.contributor.authorRammohan, Roshan Ram
dc.date.accessioned2011-02-09T20:17:11Z
dc.date.available2011-02-09T20:17:11Z
dc.date.issued2011-02-09
dc.date.submittedDecember 2010
dc.identifier.urihttp://hdl.handle.net/1928/12107
dc.description.abstractThe field of causal learning has grown in the past decade, establishing itself as a major focus in artificial intelligence research. Traditionally, approaches to causal learning are split into two areas. One area involves the learning of structures from observational data alone and the second, involves the methodologies of conducting and learning from experiments. In this dissertation, I investigate three different aspects of causal learning, all of which are based on the causal Bayesian network framework. Constraint based structure search algorithms that learn partially directed acyclic graphs as causal models from observational data rely on the faithfulness assumption, which is often violated due to inaccurate statistical tests on finite datasets. My first contribution is a modification of the traditional approaches to achieving greater robustness in the light of these faults. Secondly, I present a new algorithm to infer the parent set of a variable when a specific type of experiment called a `hard intervention' is performed. I also present an auxiliary result of this effort, a fast algorithm to estimate the Kullback Leibler divergence of high dimensional distributions from datasets. Thirdly, I introduce a fast heuristic algorithm to optimize the number and sequence of experiments required towards complete causal discovery for different classes of causal graphs and provide suggestions for implementing an interactive version. Finally, I provide numerical simulation results for each algorithm discussed and present some directions for future research.en_US
dc.language.isoen_USen_US
dc.subjectartificial intelligence, machine learning, causal learning, automated experimentation, Bayesian networksen_US
dc.subject.lcshArtificial intelligence.
dc.subject.lcshMachine learning.
dc.subject.lcshBayesian field theory.
dc.titleThree algorithms for causal learningen_US
dc.typeDissertationen_US
dc.description.degreeComputer Scienceen_US
dc.description.levelDoctoralen_US
dc.description.departmentUniversity of New Mexico. Dept. of Computer Scienceen_US
dc.description.advisorLuger, George F.
dc.description.committee-memberCaudell, Thomas Preston
dc.description.committee-memberWilliams, Lance R
dc.description.committee-memberReda Taha, Mahmoud
dc.description.committee-memberStern, Carl


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