Determining whether or not a causal conditional, such as ''If a person
has Zav's disease then they have a raised temperature'', is probably
true, or probably false, brings benefits but incurs costs. We used a v
ariant of the selection task in which there were four categories of pa
tients (e.g., those diagnosed with Zav's disease) and participants dec
ided what number of patients in each category they wished to examine.
Experiment 1 showed an effect of the seriousness of the disease on the
number of patients examined. Also, unlike performance on the standard
selection task involving a non-causal conditional, individuals wished
to search all categories of patients. Experiment 2 varied the probabi
lity of the conditional being true and the budget for examining patien
ts. Search became more selective under strong budgetary constraint. We
discuss our results in the context of recent Bayesian analyses of the
selection task and of causal inference, and propose various extension
s to this line of research.