Distribution Generalization in Underidentified Causal Models
[Virtual] Hot Topics: Foundations of Stable, Generalizable and Transferable Statistical Learning March 07, 2022 - March 10, 2022
Location: SLMath: Online/Virtual
causality
distribution generalization
robustness
identifiability
intervention
Distribution Generalization In Underidentified Causal Models
We consider the problem of predicting a response Y from a set of covariates X when test and training distributions differ. We consider a setting where such differences have causal explanations and the test distributions emerge from interventions. Causal models minimize the worst-case risk under arbitrary interventions on the covariates but may not always be identifiable from observational or interventional data. In this talk, we argue that underidentification and distribution generalization are closely connected. We propose to consider most predictive invariant models and discuss some of their properties. We also present limits of distribution generalization.
Distribution Generalization in Underidentified Causal Models
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Distribution Generalization In Underidentified Causal Models
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