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.

Science Score: 54.0%

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CITATION.cff file
Found CITATION.cff file
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codemeta.json file
Found codemeta.json file
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.zenodo.json file
Found .zenodo.json file
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DOI references
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Academic publication links
Links to: arxiv.org
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Scientific vocabulary similarity
Low similarity (12.3%) to scientific vocabulary

Keywords

causal-inference distributional-regression econometrics machine-learning randomized-controlled-trial

Last synced: 6 months ago · JSON representation ·

Repository

Basic Info

Host: GitHub
Owner: CyberAgentAILab
License: mit
Language: Jupyter Notebook
Default Branch: main
Homepage:
Size: 4.26 MB

Statistics

Stars: 7
Watchers: 0
Forks: 0
Open Issues: 5
Releases: 8

Topics

causal-inference distributional-regression econometrics machine-learning randomized-controlled-trial

Created over 1 year ago · Last pushed 6 months ago

Metadata Files

Readme Contributing License Citation

README.md

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

Install from PyPI sh pip install dte_adj
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.

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

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

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

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 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

Owner

Name: CyberAgent AI Lab
Login: CyberAgentAILab
Kind: organization
Location: Japan

Website: https://cyberagent.ai/ailab/
Twitter: cyberagent_ai
Repositories: 7
Profile: https://github.com/CyberAgentAILab

Citation (CITATION.cff)

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
type: software
title: "dte_adj: A Python Package for Estimating Distribution Treatment Effects"
version: 0.1.7
date-released: 2024-12-01
url: "https://github.com/CyberAgentAILab/python-dte-adjustment"
repository-code: "https://github.com/CyberAgentAILab/python-dte-adjustment"
abstract: "A Python package for estimating distribution treatment effects in randomized experiments. It provides APIs for conducting regression adjustment to estimate precise distribution functions, enabling deeper insights beyond average treatment effects through machine learning-enhanced estimation methods."
license: MIT
authors:
  - family-names: Byambadalai
    given-names: Undral
  - family-names: Hirata
    given-names: Tomu
  - family-names: Oka
    given-names: Tatsushi
  - family-names: Yasui
    given-names: Shota
preferred-citation:
  type: article
  title: "Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction"
  authors:
    - family-names: Byambadalai
      given-names: Undral
    - family-names: Oka
      given-names: Tatsushi
    - family-names: Yasui
      given-names: Shota
  year: 2024
  url: "https://arxiv.org/abs/2407.16037"
  repository: "arXiv:2407.16037"
references:
  - type: article
    title: "Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction"
    authors:
      - family-names: Byambadalai
        given-names: Undral
      - family-names: Oka
        given-names: Tatsushi
      - family-names: Yasui
        given-names: Shota
    year: 2024
    url: "https://arxiv.org/abs/2407.16037"
    repository: "arXiv:2407.16037"
  - type: article
    title: "On Efficient Estimation of Distributional Treatment Effects under Covariate-Adaptive Randomization"
    authors:
      - family-names: Byambadalai
        given-names: Undral
      - family-names: Hirata
        given-names: Tomu
      - family-names: Oka
        given-names: Tatsushi
      - family-names: Yasui
        given-names: Shota
    year: 2025
    url: "https://arxiv.org/abs/2506.05945"
    repository: "arXiv:2506.05945"
  - type: article
    title: "Efficient and Scalable Estimation of Distributional Treatment Effects with Multi-Task Neural Networks"
    authors:
      - family-names: Hirata
        given-names: Tomu
      - family-names: Byambadalai
        given-names: Undral
      - family-names: Oka
        given-names: Tatsushi
      - family-names: Yasui
        given-names: Shota
      - family-names: Uto
        given-names: Sho
    year: 2025
    url: "https://arxiv.org/abs/2507.07738"
    repository: "arXiv:2507.07738"

GitHub Events

Total

Create event: 32
Issues event: 4
Release event: 1
Watch event: 6
Delete event: 19
Issue comment event: 7
Push event: 63
Pull request review event: 5
Pull request event: 51

Last Year

Create event: 32
Issues event: 4
Release event: 1
Watch event: 6
Delete event: 19
Issue comment event: 7
Push event: 63
Pull request review event: 5
Pull request event: 51

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 0
Total pull requests: 29
Average time to close issues: N/A
Average time to close pull requests: 8 days
Total issue authors: 0
Total pull request authors: 2
Average comments per issue: 0
Average comments per pull request: 0.21
Merged pull requests: 15
Bot issues: 0
Bot pull requests: 15

Past Year

Issues: 0
Pull requests: 29
Average time to close issues: N/A
Average time to close pull requests: 8 days
Issue authors: 0
Pull request authors: 2
Average comments per issue: 0
Average comments per pull request: 0.21
Merged pull requests: 15
Bot issues: 0
Bot pull requests: 15

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Top Authors

Issue Authors

Pull Request Authors

TomeHirata (18)
dependabot[bot] (11)

Top Labels

Issue Labels

Pull Request Labels

dependencies (11) python (11)

Packages

Total packages: 1
Total downloads:
- pypi 71 last-month

Total dependent packages: 0
Total dependent repositories: 0
Total versions: 8
Total maintainers: 1

pypi.org: dte-adj

This is a Python library for estimating distributional treatment effects

Documentation: https://dte-adj.readthedocs.io/
License: mit
Latest release: 0.1.7
published 8 months ago

Versions: 8
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 71 Last month

Rankings

Dependent packages count: 10.6%

Average: 35.3%

Dependent repos count: 60.0%

Maintainers (1)

TomeHirata

Last synced: 6 months ago

Dependencies

.github/workflows/build-doc.yml actions

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.github/workflows/build.yml actions

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.github/workflows/publish.yml actions

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Pipfile pypi

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pyproject.toml pypi

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