Using Unsupervised Learning to Help Discover the Causal Graph
This is an incremental tool for researchers or practitioners in causal analysis to automate feature selection and generate causal graphs.
The paper tackles the problem of expediting causal discovery by developing AitiaExplorer, an exploratory causal analysis tool that uses unsupervised learning for feature selection, and finds it meets requirements by automatically selecting important features and creating causal graph candidates.
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