**Jonas Peters**

University of Copenhagen

Title: Causality and
Robust Prediction

**Abstract**

Purely predictive methods have do not perform
well when the test distribution changes too much from the training distribution.
Causal models are known to be stable with respect to distributional shifts such
as arbitrarily strong interventions on the covariates, but do not perform well
when the test distribution differs only mildly from the training distribution.
We introduce Anchor Regression, a framework that provides
a tradeoff between causal and predictive models. The method poses
different (convex and non-convex) optimization problems and relates to methods
that are tailored for instrumental variable settings.

If time allows, we show how
similar principles can be used for inferring metabolic networks. No
prior knowledge about causality is required.

**CV**

Jonas is a professor in statistics at the
Department of Mathematical Sciences at the University of Copenhagen. Previously,
he has been at the MPI for Intelligent Systems in Tubingen and was a
Marie Curie fellow at the Seminar for Statistics, ETH Zurich. He studied
Mathematics at the University of Heidelberg and the University of Cambridge. In
his research, Jonas is interested in inferring causal relationships from
different types of data and in building statistical methods that are robust
with respect to distributional shifts. He seeks to combine theory, methodology,
and applications.

http://web.math.ku.dk/~peters/index.html