iArt: A Generalized Framework for Imputation-Assisted Randomization Tests
Project description
iArt: Imputation-Assisted Randomization Tests
Authors
Jiawei Zhang*, Siyu Heng*, and Yang Feng (* indicates equal contribution)
Maintainers
Jiawei Zhang (Email: jz4721@nyu.edu), Siyu Heng (Email: siyuheng@nyu.edu), and Yang Feng (Email: yang.feng@nyu.edu)
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}
}
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