University of Amsterdam
Title: How to learn causal relations from data.
Many questions in science, policy making and everyday life are of a causal
nature: how would a change of A affect B? Causal inference, a branch of statistics and machine learning, studies how cause-effect relationships can be discovered from data and how these can be used for making predictions in situations where a system has been perturbed by an external intervention. In this talk, I will introduce the basics of two, apparently quite different, approaches to causal discovery. I will discuss how both approaches can be elegantly combined in Joint Causal Inference (JCI), a novel constraint-based approach to causal discovery from multiple data sets. This approach leads to a significant increase in the accuracy and identifiability of the predicted causal relations. One of the remaining big challenges is how to scale up the current algorithms such that large-scale causal discovery becomes feasible.
Joris Mooij is Associate Professor at the University of Amsterdam, the Netherlands. He studied mathematics and physics and received his PhD degree with honors from the Radboud University Nijmegen, the Netherlands, in 2007 on the topic of approximate inference in graphical models. Afterwards, he was a research scientist at the Max Planck Institute for Biological Cybernetics in Tubingen, Germany. In the following years, he has obtained an NWO VENI grant, an NWO VIDI grant and an ERC Starting grant on several topics in the area of causal inference. The research topics addressed by his group span the entire spectrum from causal modeling, discovery, prediction, validation and application and combine mathematical, algorithmic, statistical and modeling aspects. He has won several awards for his work.