因果分析
因果模型
贝叶斯网络
计算机科学
路径分析(统计学)
路径(计算)
偏最小二乘回归
贝叶斯概率
机器学习
人工智能
过程(计算)
因果结构
因果推理
数据挖掘
计量经济学
数学
统计
程序设计语言
物理
量子力学
操作系统
标识
DOI:10.1016/j.eswa.2009.05.021
摘要
Causal knowledge based on causal analysis can advance the quality of decision-making and thereby facilitate a process of transforming strategic objectives into effective actions. Several creditable studies have emphasized the usefulness of causal analysis techniques. Partial least squares (PLS) path modeling is one of several popular causal analysis techniques. However, one difficulty often faced when we commence research is that the causal direction is unknown due to the lack of background knowledge. To solve this difficulty, this paper proposes a method that links the Bayesian network and PLS path modeling for causal analysis. An empirical study is presented to illustrate the application of the proposed method. Based on the findings of this study, conclusions and implications for management are discussed.
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