Computer Science ETDs

Publication Date



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.




artificial intelligence, machine learning, causal learning, automated experimentation, Bayesian networks

Document Type


Degree Name

Computer Science

Level of Degree


Department Name

Department of Computer Science

First Advisor

Luger, George F.

First Committee Member (Chair)

Caudell, Thomas Preston

Second Committee Member

Williams, Lance R

Third Committee Member

Reda Taha, Mahmoud

Fourth Committee Member

Stern, Carl