Most psychological studies focused on how people reason about generative causation, in which a cause produces an effect. We here study the prevention of effects both on the general and singular level. A general prevention query might ask how strongly a vaccine is expected to reduce the risk of contracting COVID-19, whereas a singular prevention query might ask whether the absence of COVID-19 in a specific vaccinated person actually resulted from this person’s vaccination. We propose a computational model answering how knowledge about the general strength of a preventive cause can be used to assess whether a preventive link is instantiated in a singular case. We also discuss how psychological models of causal strength learning relate to mathematical models of vaccination efficacy used in medical research. The results of an experimentsuggest that many, but not all people differentiate between preventive strength and singular prevention queries, in line with the formal model.
The Effect of Causal Strength on the Use of Causal and Similarity-based Information in Feature Inference
