MeDIL is a Python package for causal factor analysis with the (me)asurement (d)ependence (i)nducing (l)atent causal model framework.
Project description
MeDIL
MeDIL is a Python package for causal factor analysis, using the measurement dependence inducing latent (MeDIL) causal model framework1. In addition to simple linear Gaussian models, MeDIL also supports deep generative models2.
More information can be found in the documentation.
Support, Bugs, and Contributing
If you have any questions, suggestions, feedback, or bugs to report, please open an issue on Gitlab or on Github or contact me.
Thanks to contributors Jeri A. Chang, Aditya Chivukula, and Mingyu Liu!
License
See LICENSE, which is the GNU Affero General Public License version 3 or later (AGPLv3+).
Changelog
See CHANGELOG for a history of the already implemented features, works in progress, and future feature ideas.
References
1. Alex Markham & Moritz Grosse-Wentrup (2020). Measurement Dependence Inducing Latent Causal Models. In Conference on Uncertainty in Artificial Intelligence (UAI) PMLR 124:590–599. URL: http://proceedings.mlr.press/v124/markham20a/markham20a.pdf.
2. Alex Markham, Mingyu Liu, Bryon Aragam, Liam Solus (2026). Neuro-Causal Factor Analysis. In Proceedings of the 13th International Conference on Probabilistic Graphical Models (PGM). PMLR.
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