python-iArt 0.1.4

iArt: A Generalized Framework for Imputation-Assisted Randomization Tests

iArt: Imputation-Assisted Randomization Tests

Authors

Jiawei Zhang*, Siyu Heng*, and Yang Feng (* indicates equal contribution)

Maintainers

Jiawei Zhang (Email: [e-mail]), Siyu Heng (Email: [e-mail]), and Yang Feng (Email: [e-mail])

Description

iArt (Imputation-Assisted Randomization Tests) is a Python package designed for conducting finite-population-exact randomization tests in design-based causal studies with missing outcomes. It offers a robust solution to handle missing data in causal inference, leveraging the potential outcomes framework and integrating various outcome imputation algorithms.

Installation

To install iArt, run the following command:

pip install python-iArt

Usage

Here is a basic example of how to use iArt:

import numpy as np
import iArt

Z = [1, 1, 1, 1, 0, 0, 0, 0]
X = [[5.1, 3.5], [4.9, np.nan], [4.7, 3.2], [4.5, np.nan], [7.2, 2.3], [8.6, 3.1], [6.0, 3.6], [8.4, 3.9]]
Y = [[4.4, 0.5], [4.3, 0.7], [4.1, np.nan], [5.0, 0.4], [1.7, 0.1], [np.nan, 0.2], [1.4, np.nan], [1.7, 0.4]]
result = iArt.test(Z=Z, X=X, Y=Y, L=1000, verbose=True)
print(result)

Detailed usage can be found here ReadDoc

Features

• Conducts finite-population-exact randomization tests.
• Handles missing data in causal inference studies.
• Supports various outcome imputation algorithms.
• Offers covariate adjustment in exact randomization tests.

Contributing

Your contributions to iArt are highly appreciated! If you're looking to contribute, we encourage you to open issues for any bugs or feature suggestions, or submit pull requests with your proposed changes.

Setting Up a Development Environment

To set up a development environment for contributing to iArt, follow these steps:

python -m venv venv
source venv/bin/activate 
pip install -r requirements.txt
python setup.py install

This creates a virtual environment (venv) for Python and activates it, allowing you to work on the package without affecting your global Python environment.

License

This project is licensed under the MIT License

Citation

If you use iArt in your research, please consider citing it:

@misc{heng2023designbased,
      title={Design-Based Causal Inference with Missing Outcomes: Missingness Mechanisms, Imputation-Assisted Randomization Tests, and Covariate Adjustment}, 
      author={Siyu Heng and Jiawei Zhang and Yang Feng},
      year={2023},
      eprint={2310.18556},
      archivePrefix={arXiv},
      primaryClass={stat.ME}
}