Sub-graph ensembles for efficient causal discovery in high-dimensional datasets

This thesis presents an efficient algorithm for identifying likely sub-graphs of a causal graph from the given data set, providing a basis for representing an unknown graph using an ensemble of sub-graphs and thus providing an efficient method of learning causal graphs in high dimensions.