Add NeurIPS paper to the reference list (#79)

* Add NeurIPS paper to the reference list

* Update docs/source/api_reference.rst

Co-authored-by: Copilot <[email protected]>

---------

Co-authored-by: Copilot <[email protected]>

Author: TomeHirata

CITATION.cff

@@ -18,8 +18,8 @@ authors:
   - family-names: Yasui
     given-names: Shota
 preferred-citation:
-  type: article
-  title: "Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction"
+  type: conference-paper
+  title: "Estimating distributional treatment effects in randomized experiments: machine learning for variance reduction"
   authors:
     - family-names: Byambadalai
       given-names: Undral
@@ -28,11 +28,14 @@ preferred-citation:
     - 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"
-  repository: "arXiv:2407.16037"
 references:
-  - type: article
-    title: "Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction"
+  - type: conference-paper
+    title: "Estimating distributional treatment effects in randomized experiments: machine learning for variance reduction"
     authors:
       - family-names: Byambadalai
         given-names: Undral
@@ -41,8 +44,11 @@ references:
       - 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"
-    repository: "arXiv:2407.16037"
   - type: article
     title: "On Efficient Estimation of Distributional Treatment Effects under Covariate-Adaptive Randomization"
     authors:
@@ -73,3 +79,17 @@ references:
     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: 2024
+    url: "https://arxiv.org/abs/2509.15594"
+    repository: "arXiv:2509.15594"

README.md

@@ -25,24 +25,32 @@ Examples of how to use this package are available in [this Get-started Guide](ht
 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)
+- **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)
 
 ### 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)
+- **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)
 
 ### 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)
 
+### 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)
+
 ## 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},
+@inproceedings{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}
+  booktitle={Proceedings of the 41st International Conference on Machine Learning},
+  articleno={199},
+  numpages={32},
+  year={2024},
+  publisher={JMLR.org},
+  series={ICML'24},
+  location={Vienna, Austria}
 }
 ```
 

docs/source/api/local.rst

@@ -1,7 +1,11 @@
 Local Distribution Estimators
 ==============================
 
-This page documents local distribution treatment effect estimators that compute treatment effects weighted by treatment propensity within each stratum. These estimators are particularly useful for handling treatment assignment heterogeneity across strata.
+This page documents local distribution treatment effect estimators that compute treatment effects weighted by treatment propensity within each stratum. These estimators are particularly useful for handling treatment assignment heterogeneity across strata and scenarios with imperfect compliance.
+
+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>`_.
 
 SimpleLocalDistributionEstimator
 --------------------------------

docs/source/api_reference.rst

@@ -14,16 +14,18 @@ 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 and Byambadalai et al. (2025) [#car2025]_ for covariate-adaptive randomization.
+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 multi-task learning approaches that train models for all locations simultaneously (using ``is_multi_task=True``), see the neural network framework in [#multitask2025]_.
 
-.. [#simple2024] Byambadalai, U., Oka, T., & Yasui, S. (2024). Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction. arXiv preprint `arXiv:2407.16037 <https://arxiv.org/abs/2407.16037>`_.
+.. [#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. arXiv preprint `arXiv:2506.05945 <https://arxiv.org/abs/2506.05945>`_.
+.. [#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>`_.
+
 Detailed Documentation
 ----------------------
 

docs/source/index.rst

@@ -30,13 +30,16 @@ 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]_
 
-.. [#simple2024] Byambadalai, U., Oka, T., & Yasui, S. (2024). Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction. arXiv preprint `arXiv:2407.16037 <https://arxiv.org/abs/2407.16037>`_.
+.. [#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. arXiv preprint `arXiv:2506.05945 <https://arxiv.org/abs/2506.05945>`_.
+.. [#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>`_.
+
 .. toctree::
    :maxdepth: 1
    :caption: Contents: