The hypothesis-discovery methods are universal; you just need to feed them data. My view is that the hard part is picking what data to feed them, and what to do with the models they discover.

Edit: I should specify, the models discovered grow in complexity based on the data provided, and so it's very difficult to go meta (i.e. run hypothesis discovery on the hypotheses you've discovered), because the amount of data you need grows very rapidly.