Abstract
In a series of papers, we have argued that causal cognition has coevolved with the use of various tools. Animals use tools, but only as extensions of their own bodies, while humans use tools that act at a distance in space and time. This means that we must learn new types of causal mappings between causes and effects. The aim of this article is to account for what is required for such learning of causal relations. Following a proposal by Grush and Springle, we argue that learning of inverse mappings from effects to causes is central. Learning such mappings also involves constraints based on monotonicity, continuity and convexity. In order for causal thinking to extend beyond space and time, mental simulations are required that predict the effects of actions. More advanced forms of causal reasoning involve more complicated forms of simulations.