This paper reports on an algorithmic exploration of the theory of causal regularity based on Mackie’s theory of causes as MINUS conditions, i.e., minimal insufficient but necessary member of a set of conditions that, though unnecessary, are sufficient for the effect. We describe the algorithm to extract causal hypotheses according to this model and the results of its application to a number of real world data sets. Results suggest further promising applications, modifications and extensions that might derive further insights of a dataset.