I-ART (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.
To install I-ART, run the following command:
pip install py-i-artHere is a basic example of how to use I-ART:
import numpy as np
from i_art import iartest
Z = np.array([1, 1, 1, 1, 0, 0, 0, 0])
X = np.array([[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 = np.array([[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 = iartest(Z=Z, X=X, Y=Y, L=1000, verbose=True)- 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.
Your contributions to I-ART 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.
To set up a development environment for contributing to I-ART, follow these steps:
python -m venv venv
source venv/bin/activate
pip install -r requirements.txtThis creates a virtual environment (venv) for Python and activates it, allowing you to work on the package without affecting your global Python environment.
This project is licensed under the MIT License
If you use I-ART 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}
}