Multicausal Inference. Evaluation of Evidence in Causally Complex Situations

We investigated the types of information that people use in making inferences about causality in uncertain situations where there are many potentially causal factors and found that a model previously proposed for unicausal inference (Schustack & Sternberg, 1981) could be extended to the multicausal case. Both multicausal and unicausal inference rely primarily on four types of evidence concerning the sufficiency and necessity of possible causal events. In multicausal inference, people also consider the representativeness, or resemblance, of the events in a situation to causal models suggested by previous situations. When evaluating multicausal problems presented in either abstract or concrete terms, most people average the unicausal likelihoods of all the events in a situation and adjust for the situation's representativeness. However, when evaluating concrete problems, some people base their multicausal estimates only on the unicausal likelihood for the most likely causal event and the situation's representativeness.