Metadata-Version: 2.1
Name: python-iArt
Version: 1.0.3
Summary: iArt: A Generalized Framework for Imputation-Assisted Randomization Tests
Home-page: https://github.com/Imputation-Assisted-Randomization-Tests/iArt-py
Author: Siyu Heng, Jiawei Zhang, and Yang Feng
Author-email: siyuheng@nyu.edu,jz4721@nyu.edu,yang.feng@nyu.edu
License: UNKNOWN
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)
        
        ## iArt (Imputation-Assisted Randomization Tests) Description
        
        Design-based causal inference, also known as randomization-based or finite-population causal inference, is one of the most widely used causal inference frameworks, largely due to the merit that its statistical validity can be guaranteed by the study design (e.g., randomized experiments) and does not require assuming specific outcome-generating distributions or super-population models. Despite its advantages, design-based causal inference can still suffer from other data-related issues, among which outcome missingness is a prevalent and significant challenge. This work systematically studies the outcome missingness problem in design-based causal inference. First, we propose a general and flexible outcome missingness mechanism that can facilitate finite-population-exact randomization tests for the null effect. Second, under this flexible missingness mechanism, we propose a general framework called ``imputation and re-imputation" for conducting finite-population-exact randomization tests in design-based causal inference with missing outcomes. This framework can incorporate any imputation algorithms (from linear models to advanced machine learning-based imputation algorithms) while ensuring finite-population-exact type-I error rate control. Third, we extend our framework to conduct covariate adjustment in randomization tests and construct finite-population-valid confidence sets with missing outcomes. Our framework is evaluated via extensive simulation studies and applied to a large-scale randomized experiment. Corresponding \textsf{Python} and \textsf{R} packages are also developed.
        
        ## Installation
        
        To install [iArt](https://pypi.org/project/python-iArt/), run the following command:
        
        ```bash
        pip install python-iArt
        ```
        
        ## Usage
        
        Here is a basic example of how to use iArt:
        
        ```python
        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](https://iart.readthedocs.io/en/latest/)
        
        ## 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:
        
        ```bash
        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:
        
        ```code
        @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}
        }
        ```
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.8
Description-Content-Type: text/markdown
