| Abstract |
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. The
ability to reliably make such causal predictions is of great value for
practical applications in a variety of disciplines. The standard method to
discover causal relations is by using experimentation. Over the last decades,
alternative methods have been proposed: constraint-based causal discovery
methods can sometimes infer causal relations from certain statistical patterns
in purely observational data. In this talk, I will introduce the basics of both
approaches to causal discovery. I will discuss how these different ideas 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. |