 Within the philosophy of biology, for example, it is well recognised that big data facilitates effective extraction of patterns and trends, and that being able to model and predict how an organism or ecosystem may behave in the future is of great importance, particularly within more applied fields such as biomedicine or conservation science. At the same time, researchers are interested in understanding the reasons for observed correlations, and typically use predictive patterns as heuristics to explore, develop and verify causal claims about the structure and functioning of entities and processes. Emanuele Ratti (2015) has argued that big data mining within genome-wide association studies often used in cancer genomics can actually underpin mechanistic reasoning, for instance by supporting eliminative inference to develop mechanistic hypotheses and by helping to explore and evaluate generalisations used to analyse the data. In a similar vein, Pietsch (2016) proposed to use variational induction as a method to establish what counts as causal relationships among big data patterns, by focusing on which analytic strategies allow for reliable prediction and effective manipulation of a phenomenon.