A Bayesian local causal discovery framework

This work introduces the Bayesian local causal discovery framework, a method for discovering unconfounded causal relationships from observational data. It addresses the hypothesis that causal discovery using local search methods will outperform causal discovery algorithms that employ global search in the context of large datasets and limited computational resources. Several Bayesian local causal discovery (BLCD) algorithms are described and results presented comparing them with two well-known global causal discovery algorithms PC and FCI, and a global Bayesian network learning algorithm, the optimal reinsertion (OR) algorithm which was post-processed to identify relationships that under assumptions are causal.; Methodologically, this research formalizes the task of causal discovery from observational data using a Bayesian approach and local search. It specifically investigates the so called Y structure in causal discovery and classifies the various types of Y structures present in the data generating networks. It identifies the Y structures in the Alarm, Hailfinder, Barley, Pathfinder and Munin networks and categorizes them. A proof of the convergence of the BLCD algorithm based on the identification of Y structures, is also provided. Principled methods of combining global and local causal discovery algorithms to improve upon the performance of the individual algorithms are discussed. In particular, a post-processing method for identifying plausible causal relationships from the output of global Bayesian network learning algorithms is described, thereby extending them to be causal discovery algorithms.; In an experimental evaluation, simulated data from synthetic causal Bayesian networks representing five different domains, as well as a real-world medical dataset, were used. Causal discovery performance was measured using precision and recall. Sometimes the local methods performed better than the global methods, and sometimes they did not (both in terms of precision/recall and in terms of computation time). When all the datasets were considered in aggregate, the local methods (BLCD and BLCDpk) had higher precision. The general performance of the BLCD class of algorithms was comparable to the global search algorithms, implying that the local search algorithms will have good performance on very large datasets when the global methods fail to scale up. The limitations of this research and directions for future research are also discussed.