Throughout our lives, we are faced with a variety of causal reasoning problems. Arguably, the most successful models of causal reasoning, Causal Graphical Models (CGMs), perform well in some situations, but there is considerable variation in how well they are able to account for data, both across scenarios and between individuals. We propose a model of causal reasoning based on quantum probability (QP) theory that accounts for behavior in situations where CGMs fail. Whether QP or classical models are appropriate depends on the representation of events constructed by the reasoner. We describe an experiment that suggests the representation of events can change with experience to become more classical, and that the representation constructed can vary between individuals, in a way that correlates with a simple measure of cognitive ability, The Cognitive Reflection Task.