https://github.com/apoorvalal/aipyw
minimal, fast, object-oriented implementation of the AIPW and related estimators for many discrete treatments. Implemented with scikitlearners and cross-fitting.
https://github.com/apoorvalal/aipyw
causal-inference doubleml python
Last synced: about 2 months ago
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minimal, fast, object-oriented implementation of the AIPW and related estimators for many discrete treatments. Implemented with scikitlearners and cross-fitting.
- Host: GitHub
- URL: https://github.com/apoorvalal/aipyw
- Owner: apoorvalal
- Created: 2022-11-04T00:08:19.000Z (over 2 years ago)
- Default Branch: master
- Last Pushed: 2025-02-22T22:20:54.000Z (3 months ago)
- Last Synced: 2025-04-12T23:38:18.898Z (about 2 months ago)
- Topics: causal-inference, doubleml, python
- Language: Jupyter Notebook
- Homepage: https://apoorvalal.github.io/aipyw/aipyw.html
- Size: 143 KB
- Stars: 3
- Watchers: 2
- Forks: 1
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# `aipyw`: Auto-DML with scikit-learn
minimal, fast object oriented implementation of the AIPW/Auto-DML with scikitlearners and cross-fitting.
Examples in `notebooks/00_demo.ipynb`.
TODO
- [X] implement automatic Riesz representer estimation (ridge, and kernel ridge)
- [ ] Add more examples
- [ ] Test with sparse categorical DGP