Counterfactual thinking, where one envisions alternative pos- sible events and their outcomes, is hypothesized to be one of the primary ways in which we reason about causal relation- ships (e.g., Pearl, 2000; Woodward, 2003). Recent compu- tational and experimental work suggests that both adults and children may reason about causality in a manner consistent with probabilistic graphical models – coherent, complex rep- resentations of causal structure that allow distinctive kinds of inferences (e.g., Gopnik et al., 2004; Griffiths & Tenenbaum, 2009). In particular, the causal models approach supports and distinguishes two types of inferences, predictions, on the one hand, and interventions, including counterfactual inter- ventions, on the other. In predictions, we take what we think is true now as a premise and then use the model to calculate what else will be true. In counterfactuals, we take some value of the model that we currently think is not true as a premise, and calculate what would follow if it were.