Models have been extensively analysed in economic methodology, notably their degree of ability to provide explanations. This paper takes a complementary, comparative approach, examining theory development in the natural sciences. Examples show how diverse types of evidence combine with causal hypotheses to generate empirically based causal theories—a cumulative process occurring over a long timescale. Models are typically nested within this broader theory. This could be a good model for research in economics, providing a methodology that ensures good correspondence with the target system—especially as economics research is largely empirical, and has effective methods for causal inference. This paper analyses the key features of three successful theories in the natural sciences, and draws out some lessons that may be useful to economists. Some examples of good practice in economics are noted, e.g. involving money and banking, and the growth of the state. On the other hand, the widespread pre-crisis use of dynamic stochastic general equilibrium (DSGE) models that ignored the financial sector raises the question, how to realise what has been omitted? Nesting models in an empirically based causal theory could solve this. Furthermore, some phenomena have clear explanations, but mainstream theory obscures them, as with the Lucas puzzle about the direction of international capital flows. And, the prevailing theories about capitalist growth do not explain the basic evidence on its temporal and spatial distribution. Economics could beneficially learn from the natural sciences.