From 1edfc2bc3a26db3274ec2030791b72b5af56ba9c Mon Sep 17 00:00:00 2001
From: TomeHirata <tomu.hirata@gmail.com>
Date: Fri, 8 Aug 2025 19:20:43 +0900
Subject: [PATCH] add citation.cff

Signed-off-by: TomeHirata <tomu.hirata@gmail.com>
---
 CITATION.cff | 75 ++++++++++++++++++++++++++++++++++++++++++++++++++++
 README.md    | 28 ++++++++++++++++++++
 2 files changed, 103 insertions(+)
 create mode 100644 CITATION.cff

diff --git a/CITATION.cff b/CITATION.cff
new file mode 100644
index 0000000..000f7eb
--- /dev/null
+++ b/CITATION.cff
@@ -0,0 +1,75 @@
+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: Taiki
+  - 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: Taiki
+      - 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: Taiki
+      - 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"
diff --git a/README.md b/README.md
index ce1c0be..cf43bc0 100644
--- a/README.md
+++ b/README.md
@@ -20,6 +20,34 @@
 ## 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.