Causal Bayes Nets in Causal Learning and Inference
Causal Bayes Nets in Causal Learning and Inference
复制标题
因果学习和推理中的因果贝叶斯网络
DOI：
10.11225/jcss.24.79
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
斎藤 元幸
中科院分区：
文献类型：
--
作者：
吉村宏之;山内光陽;増尾貞弘;斎藤 元幸
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 recognized 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 exceptions (eg, 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.
