The word aitia comes from the Ancient Greek word αιτία used in early Philosophy to mean cause.
- AitiaExplorer is an experimental causal discovery exploration tool.
- It allows a user to explore selections of features in a dataset for causal connections.
- AitiaExplorer takes in a random dataset in the form a Pandas Dataframe.
- The important features of the dataset can be extracted automatically using a variety of unsupervised learning methods.
- AitiaExplorer will automatically discovery causal graphs based on subsets of features using a selection of causal discovery algorithms (see below).
- The best performing causal graph will be returned for further analysis.
- AitiaExplorer will work with data where a causal model is known but can also automatically generate an approximate causal graph where no graph is known.
For examples of how AitiaExplorer can be used, please see the Jupyter Notebooks in the notebooks folder.
You will need
- Python 3.7+
- pip
To view the Jupyter Notebook examples, you will also need Anaconda installed.
Clone this repository:
git clone [email protected]:corvideon/aitiaexplorer.git
Change to the directory contain AitiaExplorer:
cd aitiaexplorer
Install the python dependencies using pip:
pip install -r /path/to/requirements.txt
Internally, AitiaExplorer uses multiple libraries that support causal discovery.
The main libraries used are:
- py-causal: https://github.com/bd2kccd/py-causal
- CausalGraphicalModels: https://github.com/ijmbarr/causalgraphicalmodels
- pyAgrum: https://agrum.gitlab.io/pages/pyagrum.html
AitiaExplorer is released under the FreeBSD License.
Copyright (c) 2020, Seamus Brady <[email protected]>
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
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(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
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