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.