  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
