It provides a unified view of two inductive problems—across-object and across-feature generalization—that are often considered separately. it acknowledges the importance of unknown but causally relevant variables, and uses taxonomic relationships to constrain inferences about the effects of these variables. can handle novel features that are causally linked to known features. the model helps to explain how counterfactual inferences are made in settings that simultaneously draw on relationships between objects and relationships between features.