著者
斎藤 元幸
出版者
日本認知科学会
雑誌
認知科学 (ISSN:13417924)
巻号頁・発行日
vol.24, no.1, pp.79-95, 2017-03-01 (Released:2017-09-01)
参考文献数
106
被引用文献数
1

Causal knowledge enables us to explain past events, to control present environment,and to predict future outcomes. Over the last decade, causal Bayes nets have been rec-ognized as a normative framework for causality and used as a psychological model to account for human causal learning and inference. This article provides an introduction to causal Bayes nets. According to causal Bayes nets, causal inference can be divided into three processes: (a) learning the structure of the causal network, (b) learning the strength of the causal relations, and (c) inferring the effect from the cause or the cause from the effect. For each process, I describe the predictions of causal Bayes nets, review experimental results, and suggest future directions. Although there are a few excep-tions (e.g., Markov violation), most of the results are consistent with the predictions of causal Bayes nets. The current problems of the Bayesian approach and its future perspective are discussed.

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