Causal discounting in the presence of a stronger cue is due to bias.

People use information about the covariation between a putative cause and an outcome to determine whether a causal relationship obtains. When there are two candidate causes and one is more strongly related to the effect than is the other, the influence of the second is underestimated. This phenomenon is called causal discounting. In two experiments, we adapted paradigms for studying causal learning in order to apply signal detection analysis to this phenomenon. We investigated whether the presence of a stronger alternative makes the task more difficult (indexed by differences in d') or whether people change the standard by which they assess causality (measured by beta). Our results indicate that the effect is due to bias.