Future expert systems for understanding physical mechanisms will probably employ causal models as the foundation of their expertise, and the problem of acquiring these causal models is important. This paper explores one possibility, that of acquiring causal models by understanding natural language explanations of these mechanisms. It identifies six different research issues in which understanding an explanation requires knowledge-based reasoning, and proposes approaches to these problems within an integrated natural language-based causal model acquisition system.