Applying, developing and evaluating Bayesian Network structure learning algorithms to complex real-world datasets .

There has been continued advances of machine learning techniques to learn causal models (Bayesian Networks) from data in order to understand complex real world systems and predict the effect of interventions in them. However much of the progress has been on learning and evaluating synthetic models, with many real-world aspects such as missing or noisy data, dynamic evolution of the system and unmeasured variables being relatively neglected. Moreover, there has been less focus on integrating machine learning with expert knowledge and experimental interventions, as well as explaining why the machine learning algorithms produce the models that they do. This research will focus on addressing these issues in order to produce better and more explainable causal models of real-world systems in, for example, the health, social and environmental domains.