An intuitive theory is a system of abstract concepts and laws relating those concepts that together provide a framework for explaining some domain of phenomena. Constructing an intuitive theory based on observing the world, as in building a scientific theory from data, confronts learners with a “chicken-and-egg” problem: the laws can only be expressed in terms of the theory’s core concepts, but these concepts are only meaningful in terms of the role they play in the theory’s laws; how is a learner to discover appropriate concepts and laws simultaneously, knowing neither to begin with? Even knowing the number of categories in a theory does not resolve this problem: without knowing how individuals should be sorted (which categories each belongs to), a the causal relationships between categories cannot be resolved. We explore how children can solve this chicken-and-egg problem in the domain of magnetism, drawing on perspectives from history of science, computational modeling, and behavioral experiments. We present preschoolers with a simplified magnet learning task and show how our empirical results can be explained as rational inferences within a Bayesian computational framework.