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Three algorithms for causal learning


Please use this identifier to cite or link to this item: http://hdl.handle.net/1928/12107

Three algorithms for causal learning

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dc.contributor.author Rammohan, Roshan Ram
dc.date.accessioned 2011-02-09T20:17:11Z
dc.date.available 2011-02-09T20:17:11Z
dc.date.issued 2011-02-09
dc.date.submitted December 2010
dc.identifier.uri http://hdl.handle.net/1928/12107
dc.description.abstract The 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.iso en_US en_US
dc.subject artificial intelligence, machine learning, causal learning, automated experimentation, Bayesian networks en_US
dc.subject.lcsh Artificial intelligence.
dc.subject.lcsh Machine learning.
dc.subject.lcsh Bayesian field theory.
dc.title Three algorithms for causal learning en_US
dc.type Dissertation en_US
dc.description.degree Computer Science en_US
dc.description.level Doctoral en_US
dc.description.department University of New Mexico. Dept. of Computer Science en_US
dc.description.advisor Luger, George F.
dc.description.committee-member Caudell, Thomas Preston
dc.description.committee-member Williams, Lance R
dc.description.committee-member Reda Taha, Mahmoud
dc.description.committee-member Stern, Carl

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