[Statlist] Research Seminar in Statistics *FRIDAY 25 NOVEMBER 2022* GSEM, University of Geneva

[gsem-support-instituts]

Dear All,

We are pleased to invite you to our next Research Seminar.

Looking forward to seeing you,


Organized by Professor Sebastian Engelke on behalf of the Research Center for Statistics (https://www.unige.ch/gsem/en/research/institutes/rcs/)


FRIDAY 25 NOVEMBER 2022 at 11:15am, Uni-Mail M 5220 & ONLINE
Zoom research webinar: https://unige.zoom.us/j/92924332087?pwd=U1U1NFk4dTFCRHBMeWYrSDBQcXBiQT09
Meeting ID: 929 2433 2087
Passcode: 399192

Distribution Generalization and Identifiability in IV Models
Niklas Andreas PFISTER, University of Copenhagen, Denmark
https://www.math.ku.dk/english/staff/?pure=en/persons/655303

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.


Visit the website: https://www.unige.ch/gsem/en/research/seminars/rcs/