python-dte-adjustment/CITATION.cff at main · CyberAgentAILab/python-dte-adjustment · GitHub

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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: 0.1.8
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: conference-paper
  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
  conference:
    name: "Proceedings of the 41st International Conference on Machine Learning"
  publisher:
    name: "JMLR.org"
  url: "https://arxiv.org/abs/2407.16037"
references:
  - type: conference-paper
    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
    conference:
      name: "Proceedings of the 41st International Conference on Machine Learning"
    publisher:
      name: "JMLR.org"
    url: "https://arxiv.org/abs/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"
  - type: article
    title: "Beyond the Average: Distributional Causal Inference under Imperfect Compliance"
    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/2509.15594"
    repository: "arXiv:2509.15594"