Correctly assessing the consequences of events is essential for
a successful interaction with the world. It not only requires a
causal understanding of the world but also the ability to distinguish
whether a given event is the result of an agent’s own
action (intervention) or simply the consequence of the world
being in action (observation). Previous studies have shown
that humans can learn causal structures, and that they can distinguish
interventions from observations. These studies almost
exclusively focused on structures where interventions led to a
simple forward conditioned inference problem. We tested human
subjects in a prediction game that required the integration
over hidden causes, using a betting mechanism that allowed us
to monitor subjects’ beliefs. Subjects learned the causal structure
and the conditional probabilities with appropriate feedback.
Once learned, all but one were immediately able to correctly
predict the causal effects of their interventions according
to optimal causal reasoning.