With the advent of online publishing of scientific research came an avalanche of electronic resources and repositories containing knowledge encoded in some form or another. In the domain of biomedical sciences, research is now being published at a faster-than-ever pace, with several thousand articles per day. It is impossible for any human being to process that amount of information in due time, let alone apply it to their own needs. Thus appeared the necessity of being able to automatically retrieve relevant documents and extract useful information from text. Although it is now possible to distil essential factual knowledge from text, it is difficult to interpret the connections between the extracted facts. These connections, also known as discourse relations, make the text coherent and cohesive, and their automatic discovery can lead to a better understanding of the conveyed knowledge. One fundamental discourse relation is causality, as it is the one which explains reasons and allows for inferences to be made. This thesis is the first comprehensive study which focusses on recognising discourse causality in biomedical scientific literature. We first construct a manually annotated corpus of discourse causality and analyse its characteristics. Then, a methodology for automatically recognising causal relations using text mining and natural language processing techniques is presented. Furthermore, we investigate the automatic identification of additional information about the polarity, certainty, knowledge type and source of causal relations. The entire methodology is evaluated by empirical experiments, whose results show that it is possible to successfully extract causal relations from biomedical literature. Finally, we provide an example of a direct application of our research and offer ideas for further research directions and possible improvements to our methodology.