• Corpus ID: 221857377

Using Unsupervised Learning to Help Discover the Causal Graph

@article{Brady2020UsingUL,
  title={Using Unsupervised Learning to Help Discover the Causal Graph},
  author={Seamus Brady},
  journal={ArXiv},
  year={2020},
  volume={abs/2009.10790}
}
  • Seamus Brady
  • Published 22 September 2020
  • Computer Science
  • ArXiv
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… 

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