Efficient Inference on High-Dimensional Linear Models With Missing Outcomes
Project description
Efficient Inference on High-Dimensional Linear Models With Missing Outcomes
This package implements the proposed debiasing method for conducting valid inference on the high-dimensional linear regression function with missing outcomes. We also document all the code for the simulations and real-world applications in our paper here.
- Free software: MIT license
- Python Package Documentation: https://debias-infer.readthedocs.io.
- You may also consider using our R package DebiasInfer, though the Python package will be computationally faster.
Installation guide
Debias-Infer
requires Python 3.8+ (earlier version might be applicable), NumPy, SciPy, scikit-learn, CVXPY, statsmodels. To install the latest version of Debias-Infer
from this repository, run:
python setup.py install
To pip install a stable release, run:
pip install Debias-Infer
References
[1] Y. Zhang, A. Giessing, Y.-C. Chen (2023+) Efficient Inference on High-Dimensional Linear Models with Missing Outcomes.
[2] T. Sun and C.-H. Zhang (2012). Scaled Sparse Linear Regression. Biometrika, 99, no.4: 879-898.
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