Fix the year of LDTE paper

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"

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
 

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 <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>`_.
 
 SimpleLocalDistributionEstimator
 --------------------------------

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 <https://arxiv.org/abs/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 <https://arxiv.org/abs/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 <https://arxiv.org/abs/2509.15594>`_.
 
 Detailed Documentation
 ----------------------

docs/source/index.rst

@@ -30,15 +30,15 @@ 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 <https://arxiv.org/abs/2407.16037>`_.
 
 .. [#car2025] Byambadalai, U., Hirata, T., Oka, T., & Yasui, S. (2025). On Efficient Estimation of Distributional Treatment Effects under Covariate-Adaptive Randomization. In Proceedings of the 42nd International Conference on Machine Learning (ICML'25). `arXiv:2506.05945 <https://arxiv.org/abs/2506.05945>`_.
 
 .. [#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 <https://arxiv.org/abs/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 <https://arxiv.org/abs/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 <https://arxiv.org/abs/2509.15594>`_.
 
 .. toctree::
    :maxdepth: 1