How To Learn Causal Relations From Data? - VVSOR - VVSOR

Vereniging voor Statistiek en Operations Research
12 April 2019

How To Learn Causal Relations From Data?

We cordially invite you to next Thematic Statistics Seminar meeting, taking place on the 12th of April (Friday) between 3 pm and 4 pm at UvA.

Speaker: Joris Mooij (UvA)
Title: How To Learn Causal Relations From Data?
Time: 15:00-16:00, 12th of April, 2019
Location: room F3.20, KdVI, UvA, 107 Science Park, Amsterdam

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.