2．1引言 在许多领域，特别在常识推理中，因果关系（causal relationships）是广泛而 深刻的。因果关系或因果知识的有效表达和推理，是人工智能研究的重要内容。
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UCMs are used as a visual notation for describing causal relationships between responsibilities of one or more use cases.
用于以可视化的方式描述一个或多个用例中行为之间的因果关系。
参考来源 - 基于UCM的Web应用集成测试方法Correlations between the variables take the observation data for study,but causal relationships not be decided by the correlation of data completely,so the research of causal relationships has the different significance.
变量间的相关性是以观察数据为研究对象,而因果关系并不完全由数据间的相关性决定,所以对因果关系的研究具有不同的意义。
参考来源 - 医学中关于症型和症状之间因果关系的建模·2,447,543篇论文数据，部分数据来源于NoteExpress
As a person becomes more sophisticated, his conceptions of supernatural forces and causal relationships may change.
当一个人变得更加成熟，他对超自然力量和因果关系的概念可能会改变。
Causal relationships may also be affected by relatively imprecise measurements.
因果关系也许会受到不精确的测量法所影响。
In combining loads, the timing and causal relationships that exist among th, DY, and.
相结合的负荷，时机和TH, DY，以及之间存在因果关系。
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