[NeurIPS Meetup] Causal Learning

On day 4/5 of our NeurIPS Meetup week, we are pleased to present the NeurIPS Keynote “Causal Learning” by Marloes Maathuis (ETH Zürich).

Our agenda starts at 7:00pm:
Introduction by freiburg.ai and heidelberg.ai hosts
Talk (with interactive chat) about “Causal Learning”
Discussion and Q&A session with domain expert

Abstract:
Causal reasoning is important in many areas, including the sciences, decision making and public policy. The gold standard method for determining causal relationships uses randomized controlled perturbation experiments. In many settings, however, such experiments are expensive, time consuming or impossible. Hence, it is worthwhile to obtain causal information from observational data, that is, from data obtained by observing the system of interest without subjecting it to interventions. In this talk, I will discuss approaches for causal learning from observational data, paying particular attention to the combination of causal structure learning and variable selection, with the aim of estimating causal effects. Throughout, examples will be used to illustrate the concepts.

Bio Marloes Maathuis (ETH Zurich):
Marloes Maathuis is Professor of Statistics at ETH Zurich, Switzerland. Her research focuses on causal inference, graphical models, high-dimensional statistics, and interdisciplinary applications at the interface between biology, epidemiology and statistics. She is currently program co-chair of UAI 2021, co-editor of Statistics Surveys, and associate editor for the Annals of Statistics and the Journal of the American Statistical Association. She is an IMS Fellow and received the 2020 Van Dantzig Award.

Zoom-link to event: https://zoom.us/j/94929450871