A0427
Title: Distribution generalization with instrumental variables
Authors: Niklas Pfister - University of Copenhagen (Denmark) [presenting]
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Leonard Henckel - University of Copenhagen (Denmark)
Sorawit Saengkyongam - ETH Zürich (Switzerland)
Rune Christiansen - University of Copenhagen (Denmark)
Sebastian Engelke - University of Geneva (Switzerland)
Martin Jakobsen - University of Copenhagen (Denmark)
Nicola Gnecco - University of Geneva (Switzerland)
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. 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.
