ABSTRACT
We report 4 studies which show that there are systematic quantitative patterns in the way we reason with uncertainty during causal and counterfactual inference. Two specific type of uncertainty – uncertainty about facts and about causal relations – are explored, and used to model people’s causal inferences (Studies 1-3). We then consider the relationship between causal and counterfactual reasoning, and propose that counterfactual inference can be regarded as a form of causal inference in which factual uncertainty is eradicated. On this basis we present evidence that there are also systematic quantitative patterns underlying counterfactual, as well as causal, inference (Study 4). We conclude by considering the consequences of these results for future research into causal inference.
