diff --git a/CITATION.cff b/CITATION.cff index 7bbe123..668c6a5 100644 --- a/CITATION.cff +++ b/CITATION.cff @@ -90,6 +90,6 @@ references: given-names: Tatsushi - family-names: Yasui given-names: Shota - year: 2024 + year: 2025 url: "https://arxiv.org/abs/2509.15594" repository: "arXiv:2509.15594" diff --git a/README.md b/README.md index fa445bf..acb3d1c 100644 --- a/README.md +++ b/README.md @@ -34,7 +34,7 @@ This package implements methods from the following research papers: - **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) ### Imperfect Compliance -- **Byambadalai, U., Hirata, T., Oka, T., & Yasui, S.** (2024). *Beyond the Average: Distributional Causal Inference under Imperfect Compliance*. [arXiv:2509.15594](https://arxiv.org/abs/2509.15594) +- **Byambadalai, U., Hirata, T., Oka, T., & Yasui, S.** (2025). *Beyond the Average: Distributional Causal Inference under Imperfect Compliance*. [arXiv:2509.15594](https://arxiv.org/abs/2509.15594) ## Citation diff --git a/docs/source/api/local.rst b/docs/source/api/local.rst index 9886a96..b6a4d08 100644 --- a/docs/source/api/local.rst +++ b/docs/source/api/local.rst @@ -5,7 +5,7 @@ This page documents local distribution treatment effect estimators that compute Local distribution treatment effects (LDTE) and local probability treatment effects (LPTE) provide methods for causal inference that account for treatment assignment vs. treatment receipt differences. For theoretical foundations on imperfect compliance scenarios, see: -* Byambadalai, U., Hirata, T., Oka, T., & Yasui, S. (2024). *Beyond the Average: Distributional Causal Inference under Imperfect Compliance*. `arXiv:2509.15594 `_. +* Byambadalai, U., Hirata, T., Oka, T., & Yasui, S. (2025). *Beyond the Average: Distributional Causal Inference under Imperfect Compliance*. `arXiv:2509.15594 `_. SimpleLocalDistributionEstimator -------------------------------- diff --git a/docs/source/api_reference.rst b/docs/source/api_reference.rst index aca941b..f099e73 100644 --- a/docs/source/api_reference.rst +++ b/docs/source/api_reference.rst @@ -14,7 +14,7 @@ The dte_adj package provides several types of estimators for computing distribut * **Utility Functions**: Helper functions for confidence intervals and statistical computations * **Plotting Utilities**: Visualization tools for treatment effects and distributions -For theoretical foundations, see Byambadalai et al. (2024) [#simple2024]_ for simple randomization, Byambadalai et al. (2025) [#car2025]_ for covariate-adaptive randomization, and Byambadalai et al. (2024) [#compliance2024]_ for imperfect compliance scenarios. +For theoretical foundations, see Byambadalai et al. (2024) [#simple2024]_ for simple randomization, Byambadalai et al. (2025) [#car2025]_ for covariate-adaptive randomization, and Byambadalai et al. (2024) [#compliance2025]_ for imperfect compliance scenarios. For multi-task learning approaches that train models for all locations simultaneously (using ``is_multi_task=True``), see the neural network framework in [#multitask2025]_. @@ -24,7 +24,7 @@ For multi-task learning approaches that train models for all locations simultane .. [#multitask2025] 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 preprint `arXiv:2507.07738 `_. -.. [#compliance2024] Byambadalai, U., Hirata, T., Oka, T., & Yasui, S. (2024). Beyond the Average: Distributional Causal Inference under Imperfect Compliance. arXiv preprint `arXiv:2509.15594 `_. +.. [#compliance2025] Byambadalai, U., Hirata, T., Oka, T., & Yasui, S. (2025). Beyond the Average: Distributional Causal Inference under Imperfect Compliance. arXiv preprint `arXiv:2509.15594 `_. Detailed Documentation ---------------------- diff --git a/docs/source/index.rst b/docs/source/index.rst index 703b34e..2b1ddef 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -30,7 +30,7 @@ For theoretical foundations, see: * **Simple randomization**: Byambadalai et al. (2024) [#simple2024]_ * **Covariate-adaptive randomization**: Byambadalai et al. (2025) [#car2025]_ * **Multi-task learning**: Hirata et al. (2025) [#multitask2025]_ -* **Imperfect compliance**: Byambadalai et al. (2024) [#compliance2024]_ +* **Imperfect compliance**: Byambadalai et al. (2024) [#compliance2025]_ .. [#simple2024] 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 `_. @@ -38,7 +38,7 @@ For theoretical foundations, see: .. [#multitask2025] 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 preprint `arXiv:2507.07738 `_. -.. [#compliance2024] Byambadalai, U., Hirata, T., Oka, T., & Yasui, S. (2024). Beyond the Average: Distributional Causal Inference under Imperfect Compliance. arXiv preprint `arXiv:2509.15594 `_. +.. [#compliance2025] Byambadalai, U., Hirata, T., Oka, T., & Yasui, S. (2025). Beyond the Average: Distributional Causal Inference under Imperfect Compliance. arXiv preprint `arXiv:2509.15594 `_. .. toctree:: :maxdepth: 1