Talk by Joris Mooij

Now that the summer months are over, weekly AMLab seminars are starting up again. With a change: from now on, the talks will be held on Thursdays instead of Tuesdays.

You are all cordially invited to the AMLab seminar on Thursday September 6 at 16:00 in C3.163 (FNWI, Amsterdam Science Park), where Joris Mooij will give a talk titled “Validating Causal Discovery Methods”. Afterwards there are the usual drinks and snacks.

Since the pioneering work by Peirce and Fisher, the gold standard for causal discovery is a randomized experiment. An intriguing alternative approach to causal discovery was proposed in the nineties, based on conditional independence patterns in the data. Over the past decades, dozens of causal discovery methods based on that idea have been proposed. These methods clearly work on simulated data when all their assumptions are satisfied. However, demonstrating their usefulness on real data has been a challenge. In this talk, I will discuss some of our recent attempts at validating causal discovery methods on large-scale interventional data sets from molecular biology. I will discuss a micro-array gene expression data set and a mass cytometry data set that seem perfectly suited for validation of causal discovery methods at first sight. As it turns out, however, both causal discovery on these data and the validation of such methods is more challenging than one might think initially. We find that even sophisticated modern causal discovery algorithms are outperformed by simple (non-causal) baselines on these data sets.

(joint work with Philip Versteeg and Tineke Blom)