抄録
The causal approach conflicts with the associative approach on the relation between observation and intervention in causal inference. Causal Bayes nets are unique in that they not only provide common basis for observational andinterventional knowledge but also predict the ability to derive interventional inference from observational learning and observational inference from interventional learning. Therefore, two experiments were conducted in order to test their psychological validity. In Experiment1, participants were informed about the causal structure of four variables, requested to learn the strength of causal relations from passive observations, and asked to make probabilistic inferences aboutobservation and intervention. The results replicated the previous finding that people can derive correct predictions about observation and intervention after observational trial-by-trial learning (Meder, Hagmayer, Waldmann, 2008).In Experiment 2, in which participants learned causal relations by active interventions,the results revealed inadequate sensitivity to the differences between observationand intervention in causalinference. Moreover, comparison between observational learning(Exp.1) and interventional learning (Exp.2) suggested that observations lead to more accurate estimates than interventions. The most of these results are consistent with the predictions of causal Bayes nets theory. The differences between observation and intervention are discussed.
