logistic_dml

Python implementation of "Double/Debiased Machine Learning for Logistic Partially Linear Model" by Molei Liu, Yi Zhang and Doudou Zhou. See https://academic.oup.com/ectj/article/24/3/559/6296639

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Documentation and Maintenance

logistic_dml is currently maintained by @sdamerdji.

Bugs can be reported to the issue tracker at https://github.com/sdamerdji/logistic_dml/issues

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Installation

logistic_dml requires:

• Python (>=3.9)
• Pandas (>=1.5.2)
• SciPy (>=1.9.3)
• NumPy (>=1.23.5)
• scikit-learn (>=1.3.0)

To install logistic_dml with pip use

pip install logistic_dml

logistic_dml can be installed from source via

git clone https://github.com/sdamerdji/logistic_dml.git
cd logistic_dml
pip install .

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Usage

Example:

     from logistic_dml import DML  
     Y = np.array([1, 1, 1, 1, 0, 0, 1, 0, 1, 0]*2)
     A = np.array([1, 1, 0, 0, 0, 0, 0, 0, 1, 1]*2)
     X = pd.DataFrame({'X1': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]*2,
                       'X2': [11, 12, 13, 14, 15, 16, 17, 18, 19, 20]*2})
     K = 2
     model1 = LogisticRegression()
     model2 = LinearRegression()
     result = DML(classifier=model1, regressor=model2,random_seed=0).dml(Y, A, X, k_folds=K)      

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Contributing

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Testing

After installation, you can launch the test suite from inside the source directory,

 python test_logistic_dml.py

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License

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Contact

Email: [email protected]

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Acknowledgments

Use this space to list resources you find helpful and would like to give credit to. I've included a few of my favorites to kick things off!

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