Event Date: March 16, 2020 16:15

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**Causality in Data Science **

**Joined Topical Seminar of SFB 823 and SFB 876**

**Abstract -** Causality enters data science in different ways. Often, we are interested in knowing how a system reacts under a specific intervention, e.g., when considering gene knock-outs or a change of policy.

The goal of causal discovery is to learn causal relationships from data. Other practical problems in data science focus on prediction. But as soon as we want to predict in a scenario that differs from the one which generated the available data (we may think about a different country or experiment), it might still be beneficial to apply causality related ideas. We present assumptions, under which causal structure becomes identifiable from data and methods that are robust under distributional shifts. No knowledge of causality is required.

**Short bio -** Jonas is a professor of statistics at the Department of Mathematical Sciences at the University of Copenhagen. Previously, he has associate professor at the same department, a group leader at the Max-Planck-Institute for Intelligent Systems in Tuebingen and a Marie Curie fellow (postdoc) at the Seminar for Statistics, ETH Zurich. He studied mathematics at the University of Heidelberg and the University of Cambridge and did his PhD both at the MPI Tuebingen and ETH Zurich. He tries to infer causal relationships from different types of data and is interested in building statistical methods that are robust with respect to distributional shifts. In his research, Jonas seeks to combine theory, methodology, and applications. His work relates to areas such as computational statistics, causal inference, graphical models, independence testing or high-dimensional statistics.

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