diff --git a/CITATION.cff b/CITATION.cff index 40d0a1f..ad7b83c 100644 --- a/CITATION.cff +++ b/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" diff --git a/README.md b/README.md index 7a9333b..f9df44c 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.** (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 diff --git a/docs/docs/api/index.md b/docs/docs/api/index.md index e5e2711..ef4bd7c 100644 --- a/docs/docs/api/index.md +++ b/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). diff --git a/docs/docs/api/local.md b/docs/docs/api/local.md index a9131d7..ef67144 100644 --- a/docs/docs/api/local.md +++ b/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 diff --git a/docs/docs/api/simple.md b/docs/docs/api/simple.md index 28deda2..5a63543 100644 --- a/docs/docs/api/simple.md +++ b/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). diff --git a/docs/docs/api/stratified.md b/docs/docs/api/stratified.md index 8cca322..67e1c5a 100644 --- a/docs/docs/api/stratified.md +++ b/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). diff --git a/docs/docs/index.md b/docs/docs/index.md index 43c2001..5f5248a 100644 --- a/docs/docs/index.md +++ b/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).