{"id":31211480,"url":"https://github.com/cyberagentailab/python-dte-adjustment","last_synced_at":"2025-09-21T05:27:13.248Z","repository":{"id":249823203,"uuid":"814939601","full_name":"CyberAgentAILab/python-dte-adjustment","owner":"CyberAgentAILab","description":"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. 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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/).\n\n## Installation\n\n1. **Install from PyPI**\n    ```sh\n    pip install dte_adj\n    ```\n\n2. **Install from source**\n\n    ```sh\n    git clone https://github.com/CyberAgentAILab/python-dte-adjustment\n    cd python-dte-adjustment\n    pip install -e .\n    ```\n\n## Basic Usage\nExamples of how to use this package are available in [this Get-started Guide](https://cyberagentailab.github.io/python-dte-adjustment/get_started.html).\n\n## Theoretical Foundations\n\nThis package implements methods from the following research papers:\n\n### Simple Randomization\n- **Byambadalai, U., Oka, T., \u0026 Yasui, S.** (2024). *Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction*. [arXiv:2407.16037](https://arxiv.org/abs/2407.16037)\n\n### Covariate-Adaptive Randomization\n- **Byambadalai, U., Hirata, T., Oka, T., \u0026 Yasui, S.** (2025). *On Efficient Estimation of Distributional Treatment Effects under Covariate-Adaptive Randomization*. [arXiv:2506.05945](https://arxiv.org/abs/2506.05945)\n\n### Multi-Task Learning\n- **Hirata, T., Byambadalai, U., Oka, T., Yasui, S., \u0026 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)\n\n## Citation\n\nIf you use this software in your research, please cite our work:\n\n```bibtex\n@article{byambadalai2024estimating,\n  title={Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction},\n  author={Byambadalai, Undral and Oka, Tatsushi and Yasui, Shota},\n  journal={arXiv preprint arXiv:2407.16037},\n  year={2024}\n}\n```\n\nFor other citation formats, see our [CITATION.cff](CITATION.cff) file.\n\n## Development\nWe welcome contributions to the project! Please review our [Contribution Guide](CONTRIBUTING.md) for details on how to get started.\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n## Maintainers\n- [Tomu Hirata](https://github.com/TomeHirata)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcyberagentailab%2Fpython-dte-adjustment","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcyberagentailab%2Fpython-dte-adjustment","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcyberagentailab%2Fpython-dte-adjustment/lists"}