diff --git a/CITATION.cff b/CITATION.cff index fb74da6..7bbe123 100644 --- a/CITATION.cff +++ b/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" diff --git a/README.md b/README.md index cf43bc0..fa445bf 100644 --- a/README.md +++ b/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} } ``` diff --git a/docs/source/api/local.rst b/docs/source/api/local.rst index 034edf1..9886a96 100644 --- a/docs/source/api/local.rst +++ b/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 `_. SimpleLocalDistributionEstimator -------------------------------- diff --git a/docs/source/api_reference.rst b/docs/source/api_reference.rst index 97995fe..aca941b 100644 --- a/docs/source/api_reference.rst +++ b/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 `_. +.. [#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 `_. -.. [#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 `_. +.. [#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 `_. .. [#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 `_. + Detailed Documentation ---------------------- diff --git a/docs/source/index.rst b/docs/source/index.rst index b431796..703b34e 100644 --- a/docs/source/index.rst +++ b/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 `_. +.. [#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 `_. -.. [#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 `_. +.. [#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 `_. .. [#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 `_. + .. toctree:: :maxdepth: 1 :caption: Contents: