论文标题
使用无监督的学习来帮助发现因果图
Using Unsupervised Learning to Help Discover the Causal Graph
论文作者
论文摘要
本文概述的软件AitiaExplorer是一种探索性因果分析工具，它使用无监督学习进行特征选择以加快因果发现。在本文中，简要描述了因果关系的问题空间，并提供了相关研究的概述。概述了该软件的问题声明和要求。讨论了实施中的关键要求，关键设计决策和AitiaExplorer的实际实施。最后，根据前面概述的问题声明和要求评估了此实施。发现AitiaExplorer符合这些要求，并且是一种有用的探索性因果分析工具，可以自动从数据集中选择重要功能的子集，并根据这些功能创建因果图候选者，以进行审查。该软件可从https://github.com/corvideon/aitiaexplorer获得
The software outlined in this paper, AitiaExplorer, is an exploratory causal analysis tool which uses unsupervised learning for feature selection in order to expedite causal discovery. In this paper the problem space of causality is briefly described and an overview of related research is provided. A problem statement and requirements for the software are outlined. The key requirements in the implementation, the key design decisions and the actual implementation of AitiaExplorer are discussed. Finally, this implementation is evaluated in terms of the problem statement and requirements outlined earlier. It is found that AitiaExplorer meets these requirements and is a useful exploratory causal analysis tool that automatically selects subsets of important features from a dataset and creates causal graph candidates for review based on these features. The software is available at https://github.com/corvideon/aitiaexplorer
