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Explanations in Bayesian networks are usually probabilistic measures of how well a hypothesis is supported by observations. This observational based approach does not fulfill all the properties one would expect from an explanation. In particular, it does not include any notion of causality. In this thesis, we investigate how causality can be used to allow for an interpretation of explanations as statements of cause and effect, between a hypothesis and observations. We detail how factor graphs can be used as an abstraction of Bayesian networks to perform efficient probabilistic inference in Bayesian network with the aim of using these methods to find explanations. We provide a critique of existing approaches to explanation, and list the requirements for explanations that consider cause-effect relationships. We propose an algorithm for finding explanations that allows for causal interpretation as well as probabilistic. The algorithm is based on the concept of causal information flow and the search for explanations is inspired by feature selection. The proposed approach is evaluated on two sample networks. The results are compared with those of existing approaches and we conclude that our proposed algorithm finds explanations that satisfy the requirements we defined for causal explanations.