This package is for causal inference, focusing on the Measurement Dependence Inducing Latent (MeDIL) Causal Model framework.
Project description
MeDIL
MeDIL is a Python package for causal inference, focusing on the Measurement Dependence Inducing Latent (MeDIL) Causal Model framework[^fn1].
More information can be found in the documentation or on the project web page
Support, Bugs, and Conttributing
If you have any questions, suggestions, feedback, or bugs to report, please open an issue on Gitlab or on Github or contact me. Additionally, if you would like to use this package or any of its code in your research, or to contribute to this package, feel free (but not obliged) to contact me.
License
See LICENSE, which is the GNU Affero General Public License v3.
Changelog
See CHANGELOG for a history of the already implemented features, works in progress, and future feature ideas.
References
[^fn1]: Alex Markham and Moritz Grosse-Wentrup (2019). Measurement Dependence Inducing Latent Causal Models. Conference on Uncertainty in Artificial Intelligence (UAI), 2020. ISSN 2640-3498. URL http://www.auai.org/uai2020/proceedings/244_main_paper.pdf.
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