docs: sync README, docs, and CITATION.cff with definitive references

CITATION.cff

@@ -49,7 +49,7 @@ references:
     publisher:
       name: "JMLR.org"
     url: "https://arxiv.org/abs/2407.16037"
-  - type: article
+  - type: conference-paper
     title: "On Efficient Estimation of Distributional Treatment Effects under Covariate-Adaptive Randomization"
     authors:
       - family-names: Byambadalai
@@ -61,6 +61,10 @@ references:
       - family-names: Yasui
         given-names: Shota
     year: 2025
+    conference:
+      name: "Proceedings of the 42nd International Conference on Machine Learning"
+    publisher:
+      name: "JMLR.org"
     url: "https://arxiv.org/abs/2506.05945"
     repository: "arXiv:2506.05945"
   - type: article
@@ -75,11 +79,11 @@ references:
       - family-names: Yasui
         given-names: Shota
       - family-names: Uto
-        given-names: Sho
+        given-names: Shingo
     year: 2025
     url: "https://arxiv.org/abs/2507.07738"
     repository: "arXiv:2507.07738"
-  - type: article
+  - type: conference-paper
     title: "Beyond the Average: Distributional Causal Inference under Imperfect Compliance"
     authors:
       - family-names: Byambadalai
@@ -91,5 +95,9 @@ references:
       - family-names: Yasui
         given-names: Shota
     year: 2025
+    conference:
+      name: "Advances in Neural Information Processing Systems 38"
+    publisher:
+      name: "Neural Information Processing Systems Foundation, Inc. (NeurIPS)"
     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.** (2025). *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*. In Advances in Neural Information Processing Systems 38 (NeurIPS'25). [arXiv:2509.15594](https://arxiv.org/abs/2509.15594)
 
 ## Citation
 

docs/docs/api/index.md

@@ -26,4 +26,4 @@ For multi-task learning approaches that train models for all locations simultane
 [^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).
-[^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).
+[^compliance2025]: Byambadalai, U., Hirata, T., Oka, T., & Yasui, S. (2025). Beyond the Average: Distributional Causal Inference under Imperfect Compliance. In Advances in Neural Information Processing Systems 38 (NeurIPS'25). [arXiv:2509.15594](https://arxiv.org/abs/2509.15594).

docs/docs/api/local.md

@@ -4,7 +4,7 @@ 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. (2025). *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*. In Advances in Neural Information Processing Systems 38 (NeurIPS'25). [arXiv:2509.15594](https://arxiv.org/abs/2509.15594).
 
 ## SimpleLocalDistributionEstimator
 

docs/docs/api/simple.md

@@ -20,4 +20,4 @@ Byambadalai et al. (2024)[^simple2024] propose a regression adjustment method th
       inherited_members: true
       show_inheritance_diagram: true
 
-[^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).

docs/docs/api/stratified.md

@@ -18,4 +18,4 @@ Byambadalai et al. (2025)[^car2025] propose a flexible distribution regression f
     options:
       inherited_members: true
 
-[^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).

docs/docs/index.md

@@ -33,4 +33,4 @@ MIT License
 [^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).
-[^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).
+[^compliance2025]: Byambadalai, U., Hirata, T., Oka, T., & Yasui, S. (2025). Beyond the Average: Distributional Causal Inference under Imperfect Compliance. In Advances in Neural Information Processing Systems 38 (NeurIPS'25). [arXiv:2509.15594](https://arxiv.org/abs/2509.15594).