Different ways 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.
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