https://github.com/cyberagentailab/python-dte-adjustment
dte_adj is a Python package for estimating distribution treatment effects. It provides APIs for conducting regression adjustment to estimate precise distribution functions as well as convenient utils.
https://github.com/cyberagentailab/python-dte-adjustment
causal-inference distributional-regression econometrics machine-learning randomized-controlled-trial
Last synced: 9 months ago
JSON representation
dte_adj is a Python package for estimating distribution treatment effects. It provides APIs for conducting regression adjustment to estimate precise distribution functions as well as convenient utils.
- Host: GitHub
- URL: https://github.com/cyberagentailab/python-dte-adjustment
- Owner: CyberAgentAILab
- License: mit
- Created: 2024-06-14T03:01:14.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2025-09-18T10:43:41.000Z (9 months ago)
- Last Synced: 2025-09-18T12:45:00.246Z (9 months ago)
- Topics: causal-inference, distributional-regression, econometrics, machine-learning, randomized-controlled-trial
- Language: Jupyter Notebook
- Homepage:
- Size: 4.31 MB
- Stars: 7
- Watchers: 0
- Forks: 0
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Citation: CITATION.cff
Awesome Lists containing this project
README
## Overview
`dte_adj` is a Python package for estimating distribution treatment effects. It provides APIs for conducting regression adjustment to estimate precise distribution functions as well as convenient utils. For the details of this package, see [the documentation](https://cyberagentailab.github.io/python-dte-adjustment/).
## Installation
1. **Install from PyPI**
```sh
pip install dte_adj
```
2. **Install from source**
```sh
git clone https://github.com/CyberAgentAILab/python-dte-adjustment
cd python-dte-adjustment
pip install -e .
```
## Basic Usage
Examples of how to use this package are available in [this Get-started Guide](https://cyberagentailab.github.io/python-dte-adjustment/get_started.html).
## Theoretical Foundations
This package implements methods from the following research papers:
### Simple Randomization
- **Byambadalai, U., Oka, T., & Yasui, S.** (2024). *Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction*. [arXiv:2407.16037](https://arxiv.org/abs/2407.16037)
### Covariate-Adaptive Randomization
- **Byambadalai, U., Hirata, T., Oka, T., & Yasui, S.** (2025). *On Efficient Estimation of Distributional Treatment Effects under Covariate-Adaptive Randomization*. [arXiv:2506.05945](https://arxiv.org/abs/2506.05945)
### Multi-Task Learning
- **Hirata, T., Byambadalai, U., Oka, T., Yasui, S., & Uto, S.** (2025). *Efficient and Scalable Estimation of Distributional Treatment Effects with Multi-Task Neural Networks*. [arXiv:2507.07738](https://arxiv.org/abs/2507.07738)
## Citation
If you use this software in your research, please cite our work:
```bibtex
@article{byambadalai2024estimating,
title={Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction},
author={Byambadalai, Undral and Oka, Tatsushi and Yasui, Shota},
journal={arXiv preprint arXiv:2407.16037},
year={2024}
}
```
For other citation formats, see our [CITATION.cff](CITATION.cff) file.
## Development
We welcome contributions to the project! Please review our [Contribution Guide](CONTRIBUTING.md) for details on how to get started.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Maintainers
- [Tomu Hirata](https://github.com/TomeHirata)