[Statlist] Joint talk: ETH/UZH Research Seminar and ETH Young Data Science Researcher Seminar Zurich, by Niklas Pfister, University of Copenhagen, 01.12.2022

[Maurer Letizia]

We are glad to announce the following joint talk ETH/UZH Research Seminar and ETH Young Data Science Researcher Seminar Zurich:

"Distribution Generalization and Identifiability in IV Models"   
by Niklas Pfister, University of Copenhagen

Date and time: Thursday,  01.12.22 at 16.00 h
Place: ETH Zurich, HG G 19.1
Zoom link: https://ethz.zoom.us/j/68998616059, Meeting ID: 689 9861 6059

Abstract:  Causal models can provide good predictions even under distributional  shifts. This observation has led to the development of various methods  that use causal learning to improve the generalization performance of  predictive models. In this talk, we consider this type of approach for  instrumental variable (IV) models. IV allows us to identify a causal function between covariates X and a response Y,  even in the presence  of unobserved confounding. In many practical prediction settings the  causal function is however not fully identifiable. We consider two approaches for dealing with this under-identified setting: (1) By adding a sparsity constraint and (2) by introducing the invariant most predictive (IMP) model, which deals with the under-identifiability by 
selecting the most predictive model among all feasible IV solutions. Furthermore, we analyze to which types of distributional shifts these models generalize.

Seminar website: https://math.ethz.ch/sfs/news-and-events/research-seminar.html

Research Seminar – Seminar for Statistics | ETH Zurich
math.ethz.ch

and 

Young Data Science Researcher Seminar Zurich – Seminar for Statistics | ETH Zurich
math.ethz.ch

Seminar website: https://math.ethz.ch/sfs/news-and-events/young-data-science.html