https://github.com/alex-markham/medil
mirror of the MeDIL Python package for causal modeling
https://github.com/alex-markham/medil
causal-discovery causal-representation-learning deep-generative-model factor-analysis
Last synced: about 1 year ago
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mirror of the MeDIL Python package for causal modeling
- Host: GitHub
- URL: https://github.com/alex-markham/medil
- Owner: Alex-Markham
- License: agpl-3.0
- Created: 2022-10-18T11:23:07.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2024-09-24T12:57:24.000Z (almost 2 years ago)
- Last Synced: 2025-04-27T03:33:33.578Z (about 1 year ago)
- Topics: causal-discovery, causal-representation-learning, deep-generative-model, factor-analysis
- Language: Python
- Homepage: https://medil.causal.dev
- Size: 39 MB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE.md
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README
## MeDIL
MeDIL is a Python package for causal factor analysis, using the measurement dependence inducing latent (MeDIL) causal model framework[1](#medil_paper).
In addition to simple linear Gaussian models, MeDIL also supports deep generative models[2](#ncfa_paper).
More information can be found in the [documentation](https://medil.causal.dev).
### Support, Bugs, and Contributing
If you have any questions, suggestions, feedback, or bugs to report, please [open an issue on Gitlab](https://gitlab.com/alex-markham/medil/issues/new) or [on Github](https://github.com/Alex-Markham/medil/issues/new) or [contact me](https://causal.dev/#contact).
Thanks to contributors An Hui Chang, [Aditya Chivukula](https://github.com/adityachivu/), and [Mingyu Liu](https://github.com/JerryLiuMY)!
### License
See [LICENSE](https://gitlab.com/alex-markham/medil/blob/master/LICENSE.md), which is the GNU Affero General Public License version 3 or later (AGPLv3+).
### Changelog
See [CHANGELOG](https://gitlab.com/alex-markham/medil/blob/master/CHANGELOG.md) 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](http://proceedings.mlr.press/v124/markham20a/markham20a.pdf).
2. Alex Markham, Mingyu Liu, Bryon Aragam, Liam Solus (2023). Neuro-Causal Factor Analysis. *prepint*. [arXiv:2305.19802](https://arxiv.org/abs/2305.19802) [stat.ML].