dte-adj 0.1.10

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.

Installation

1. Install from PyPI

   pip install dte_adj
   

2. Install from source

   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.

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. In Proceedings of the 41st International Conference on Machine Learning (ICML'24). arXiv: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. In Proceedings of the 42nd International Conference on Machine Learning (ICML'25). arXiv: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

Imperfect Compliance

• Byambadalai, U., Hirata, T., Oka, T., & Yasui, S. (2025). Beyond the Average: Distributional Causal Inference under Imperfect Compliance. arXiv:2509.15594

Citation

If you use this software in your research, please cite our work:

@inproceedings{byambadalai2024estimating,
  title={Estimating distributional treatment effects in randomized experiments: machine learning for variance reduction},
  author={Byambadalai, Undral and Oka, Tatsushi and Yasui, Shota},
  booktitle={Proceedings of the 41st International Conference on Machine Learning},
  articleno={199},
  numpages={32},
  year={2024},
  publisher={JMLR.org},
  series={ICML'24},
  location={Vienna, Austria}
}

For other citation formats, see our CITATION.cff file.

Development

We welcome contributions to the project! Please review our Contribution Guide for details on how to get started.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Maintainers

• Tomu Hirata