Consequence-Cause Matching : Looking to the Consequences of Events to Infer Their Causes

This article documents a bias in people’s causal inferences, showing that people non-normatively consider an event’s consequences when inferring its causes. Across experiments, participants’ inferences about event causes were systematically affected by how similar (in both size and valence) those causes were to event consequences, even when the consequences were objectively uninformative about the causes. For example, people inferred that a product failure (computer crash) had a large cause (widespread computer virus) if it had a large consequence (job loss), but that the identical failure was more likely to have a smaller cause (cooling fan malfunction) if the consequence was small—even though the consequences gave no new information about what caused the crash. This “consequence-cause matching,” which can affect product attitudes, may arise because people are motivated to see the world as predictable and because matching is an accessible schema that helps them to fulfill this motivation