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Causal discounting occurs when the perceived efficacy of a putative cause is reduced by the presence of a stronger causal candidate. Previous studies of causal discounting have defined the strength of causal candidates in terms of the degree to which the cause and the effect covary (e.g., Baker, Mercier, Vallee-Tourangeau, Frank, & Pan, 1993). In contrast, in the present study, causal strength was defined in terms of both covariation- and belief-based cues. Seventy-two participants made causality judgments for a fictional causal candidate both in isolation and when paired with either a stronger or a weaker cause. The results demonstrated that the degree to which a causal candidate is discounted depends not only on the degree to which an alternative cause covaries with the effect, but also on whether the alternative is a believable or unbelievable candidate. Indeed, it was observed that a highly believable alternative will produce the discounting effect, even if it is a weaker covariate than the original candidate. These findings suggest the need to incorporate both belief-based and covariation-based cues into models of causal attribution.

The present study investigated how people combine covariation information (Cheng & Novick, 1990, 1992) with pre-existing beliefs (White, 1989) when evaluating causal hypotheses. Three experiments, using both within- and between-subjects designs, found that the use of covariation information and beliefs interacted, such that the effects of covariation were larger when people assessed hypotheses about believable than about unbelievable causal candidates. In Experiment 2, this interaction was observed when participants made judgments in stages (e.g., first evaluating covariation information about a causal candidate and then evaluating the believability of a candidate), as well as when the information was presented simultaneously. Experiment 3 demonstrated that this pattern was also reflected in participants' metacognitive judgments: Participants indicated that they weighed covariation information more heavily for believable than unbelievable candidates. Finally, Experiments 1 and 2 demonstrated the presence of individual differences in the use of covariation- and belief-based cues. That is, individuals who tended to base their causality judgments primarily on belief were less likely to make use of covariation information and vice versa. The findings were most consistent with White's (1989) causal power theory, which suggests that covariation information is more likely to be considered relevant to believable than unbelievable causes.