The impact of disablers on predictive inference

People consider alternative causes when deciding whether a cause is responsible for an effect (diagnostic inference) but appear to neglect them when deciding whether an effect will occur (predictive inference). Five experiments were conducted to test a 2-part explanation of this phenomenon: namely, (a) that people interpret standard predictive queries as requests to estimate the probability of the effect in the presence of the cause alone, which renders alternative causes irrelevant, and (b) that the impact of disablers (inhibitory causes) on predictive judgments is underestimated, and this underestimation is wrongly interpreted as cause neglect. Experiment 1 showed that standard predictive queries are frequently interpreted as requests to estimate the likelihood of E given C alone. In Experiment 2, a causal Bayes network overestimated predictive inference when it was queried in the standard way, but this overestimation diminished when predictive inference was queried using an alternative wording. In Experiment 3, participants judged alternative causes to be relevant to diagnostic inference and both disablers and alternative causes to be relevant to predictive inference. In Experiment 4, disablers greatly overshadowed alternative causes in predictive judgments, but their impact on diagnostic judgments was negligible. In Experiment 5, the order of disabler retrieval influenced causal judgments. Taken together, these results indicate that human causal inference cannot be adequately modeled unless the manner in which knowledge is retrieved and applied is taken into consideration.