docs(paper): address second-round JOSS review comments

paper/paper.bib

@@ -54,15 +54,18 @@ @inproceedings{byambadalai2025efficientestimationdistributionaltreatment
       title={On Efficient Estimation of Distributional Treatment Effects under Covariate-Adaptive Randomization},
       author={Byambadalai, Undral and Hirata, Tomu and Oka, Tatsushi and Yasui, Shota},
       booktitle={Proceedings of the 42nd International Conference on Machine Learning},
+      series={ICML'25},
+      articleno={224},
+      numpages={24},
+      location={Vancouver, Canada},
+      publisher={JMLR.org},
       year={2025},
-      series={Proceedings of Machine Learning Research},
-      publisher={PMLR},
       doi={10.2139/ssrn.5286083}
 }
 
 @misc{hirata2025efficientscalableestimationdistributional,
       title={Efficient and Scalable Estimation of Distributional Treatment Effects with Multi-Task Neural Networks},
-      author={Hirata, Tomu and Byambadalai, Undral and Oka, Tatsushi and Yasui, Shota and Uto, Shunsuke},
+      author={Hirata, Tomu and Byambadalai, Undral and Oka, Tatsushi and Yasui, Shota and Uto, Shingo},
       year={2025},
       eprint={2507.07738},
       archivePrefix={arXiv},
@@ -74,23 +77,32 @@ @misc{hirata2025efficientscalableestimationdistributional
 @inproceedings{byambadalai2025imperfectcompliance,
       title={Beyond the Average: Distributional Causal Inference under Imperfect Compliance},
       author={Byambadalai, Undral and Hirata, Tomu and Oka, Tatsushi and Yasui, Shota},
-      booktitle={Advances in Neural Information Processing Systems},
+      booktitle={Advances in Neural Information Processing Systems 38},
+      series={Advances in Neural Information Processing Systems 38},
+      pages={76359--76393},
+      publisher={Neural Information Processing Systems Foundation, Inc. (NeurIPS)},
       year={2025},
-      doi={10.2139/ssrn.5504858}
+      doi={10.52202/085713-2302}
 }
 
 @article{oka2025regression,
   title={Regression Adjustment for Estimating Distributional Treatment Effects in Randomized Controlled Trials},
   author={Oka, Tatsushi and Yasui, Shota and Hayakawa, Yuta and Byambadalai, Undral},
   journal={Econometric Reviews},
-  year={2025},
+  volume={45},
+  number={1},
+  pages={2--17},
+  year={2026},
   doi={10.1080/07474938.2025.2538843}
 }
 
 @article{yasui2026abema,
   title={Distributional Treatment Effects of Content Promotion: Evidence from an {ABEMA} Field Experiment},
   author={Yasui, Shota and Oka, Tatsushi and Byambadalai, Undral and Oishi, Yuki},
   journal={The Japanese Economic Review},
+  volume={77},
+  number={2},
+  pages={391--406},
   year={2026},
   doi={10.1007/s42973-026-00234-y}
 }
@@ -153,6 +165,14 @@ @article{finkelstein2012
   doi={10.1093/qje/qjs020}
 }
 
+@Manual{qte,
+  title  = {qte: Quantile Treatment Effects},
+  author = {Callaway, Brantly},
+  year   = {2026},
+  note   = {R package version 2.0.0},
+  url    = {https://CRAN.R-project.org/package=qte}
+}
+
 @article{kobrosly2020causalcurve,
   title={causal-curve: A Python Causal Inference Package to Estimate Causal Dose-Response Curves},
   author={Kobrosly, Roni W.},

paper/paper.md

@@ -33,13 +33,13 @@ bibliography: paper.bib
 
 `dte_adj` is a Python package for analyzing how an intervention, such as a marketing campaign, a medical treatment, or a policy change, affects the entire range of an outcome, not just its average. Practitioners running randomized experiments (RCTs, also known as A/B tests) can use it to answer questions such as "did the treatment help the users who were struggling the most?" or "did it move the whole distribution, or only shift the top end?", together with rigorous confidence bands around those answers.
 
-More formally, `dte_adj` estimates distributional treatment effects (DTEs) in randomized experiments. Unlike traditional approaches that focus on average treatment effects, `dte_adj` enables researchers to analyze the full distributional impact of interventions across different outcome levels. The package implements machine learning-enhanced regression adjustment methods for variance reduction, supports multiple experimental designs including simple randomization, covariate-adaptive randomization, and settings with imperfect compliance, and provides a scikit-learn compatible API with comprehensive functionality for computing distribution functions, probability treatment effects, and quantile treatment effects with confidence intervals.
+More formally, `dte_adj` estimates distributional treatment effects (DTEs) in randomized experiments. Unlike traditional approaches that focus on average treatment effects, `dte_adj` enables researchers to analyze the full distributional impact of interventions across different outcome levels. The package implements machine learning-enhanced regression adjustment methods for variance reduction, supports multiple experimental designs including simple randomization, covariate-adaptive randomization, and settings with imperfect compliance, and provides a scikit-learn-like API [@scikit-learn] with comprehensive functionality for computing distribution functions, probability treatment effects, and quantile treatment effects with confidence intervals.
 
 # Statement of Need
 
 Randomized experiments have been fundamental to scientific inquiry since @fisher1935design, providing the gold standard for causal inference. While most experimental analyses focus on average treatment effects (ATEs), many research questions require understanding how treatments affect the entire distribution of outcomes. Distributional treatment effects (DTEs) capture these richer patterns, revealing heterogeneous impacts across different outcome levels that averages can mask. For example, a policy intervention might have no effect on average income while substantially reducing poverty rates at lower quantiles, or a medical treatment might benefit patients at the tails of the distribution differently than those near the median.
 
-Despite the growing importance of distributional analysis in economics, medicine, and technology, the Python ecosystem lacks comprehensive tools for DTE estimation with modern variance reduction techniques. Applied researchers in economics, biostatistics, and the social sciences, along with data scientists and experimentation engineers running A/B tests in industry, are often left resorting to basic empirical CDFs or manual implementations that lack statistical rigor. `dte_adj` fills this gap for these users with a unified framework that integrates state-of-the-art machine learning methods for improved precision, rigorous confidence interval construction, and support for complex experimental designs. It complements general causal inference libraries such as `DoWhy` [@dowhy] and `EconML` [@econml], which target average or conditional average treatment effects, and the R package `qte`, which supports quantile treatment effects but without machine learning-based variance reduction.
+Despite the growing importance of distributional analysis in economics, medicine, and technology, the Python ecosystem lacks comprehensive tools for DTE estimation with modern variance reduction techniques. Applied researchers in economics, biostatistics, and the social sciences, along with data scientists and experimentation engineers running A/B tests in industry, are often left resorting to basic empirical CDFs or manual implementations that do not provide the inferential guarantees implemented here. `dte_adj` fills this gap for these users with a unified framework that integrates state-of-the-art machine learning methods for improved precision, rigorous confidence interval construction, and support for complex experimental designs. It complements general causal inference libraries such as `DoWhy` [@dowhy] and `EconML` [@econml], which target average or conditional average treatment effects, and the R package `qte` [@qte], which supports quantile treatment effects but without machine learning-based variance reduction.
 
 # State of the Field
 
@@ -50,7 +50,7 @@ Several Python packages address causal inference, but none focus on distribution
 - **EconML** [@econml]: Incorporates machine learning for heterogeneous treatment effect estimation (CATE) but does not address distributional effects.
 - **causal-curve** [@kobrosly2020causalcurve]: Estimates dose-response curves but targets continuous treatments rather than distributional outcomes.
 
-In the R ecosystem, packages like `qte` provide quantile treatment effect estimation but lack machine learning integration for variance reduction. `dte_adj` uniquely combines: (1) distributional treatment effect estimation across the full outcome distribution, (2) machine learning-enhanced regression adjustment for precision gains, and (3) support for multiple experimental designs including covariate-adaptive randomization and imperfect compliance settings.
+In the R ecosystem, packages like `qte` [@qte] provide quantile treatment effect estimation but lack machine learning integration for variance reduction. `dte_adj` uniquely combines: (1) distributional treatment effect estimation across the full outcome distribution, (2) machine learning-enhanced regression adjustment for precision gains, and (3) support for multiple experimental designs including covariate-adaptive randomization and imperfect compliance settings.
 
 A standalone package is warranted because existing libraries are organized around scalar estimands (ATEs in `DoWhy`, heterogeneous CATEs in `EconML`), whereas distributional estimation requires a distinct set of primitives: distribution functions evaluated over grids of locations, interval probabilities, quantile inversion, pointwise and uniform confidence bands, and cross-fitted distributional regression for variance reduction. These do not map cleanly onto point-estimate abstractions, and a focused package also allows the implementation to track a rapidly evolving methodological literature on covariate-adaptive randomization, imperfect compliance, and multi-task learning for DTEs.
 
@@ -64,19 +64,19 @@ A standalone package is warranted because existing libraries are organized aroun
 
 The stratified and non-stratified estimators share a common API with three methods: `predict_dte()` for distributional treatment effects, `predict_pte()` for probability treatment effects over intervals, and `predict_qte()` for quantile treatment effects. The local estimators expose `predict_ldte()` and `predict_lpte()` instead, for the imperfect-compliance setting. The adjusted estimators use K-fold cross-fitting to prevent overfitting and support both single-task and multi-task learning modes [@hirata2025efficientscalableestimationdistributional] for computational efficiency. Bootstrap methods provide confidence intervals with multiple variance estimation approaches.
 
-The template method pattern is a natural fit here because every estimator shares the same outer algorithm (evaluate a distribution function on a grid, difference across treatment arms, and construct confidence bands) and differs only in how the conditional distribution is estimated, which depends on the experimental design and on whether a plug-in or a cross-fitted machine learning estimator is used. Defining the outer algorithm once in the base class keeps its statistical invariants in one place, whereas a strategy-based configuration would push that structure into runtime flags and obscure them. For the same reason the estimators are exposed as distinct classes rather than a single configurable one: many flag combinations would be confusing (e.g., stratification without strata) or correspond to different estimands with different identifying assumptions, and separate classes make the required inputs explicit at construction time and let each estimator evolve independently.
+The template method pattern is a natural fit here because every estimator shares the same outer algorithm (evaluate a distribution function on a grid, difference across treatment arms, and construct confidence bands) and differs only in how the conditional distribution is estimated, which depends on the experimental design and on whether a plug-in or a cross-fitted machine learning estimator is used. Defining the outer algorithm once in the base class keeps its statistical invariants in one place, whereas a strategy-based configuration would push that structure into runtime flags and obscure them. For the same reason, the estimators are exposed as distinct classes rather than a single configurable one: many flag combinations would be confusing (e.g., stratification without strata) or correspond to different estimands with different identifying assumptions, and separate classes make the required inputs explicit at construction time and let each estimator evolve independently.
 
 ![Distributional treatment effects for the Hillstrom email marketing dataset [@hillstrom2008], comparing Women's vs Men's email campaigns. The simple estimator (left, purple) and ML-adjusted estimator (right, green) show that adjustment substantially tightens confidence bands, demonstrating the variance reduction benefit of regression adjustment.](hillstorm_dte.png)
 
 ![Local distributional treatment effects for emergency department costs in the Oregon Health Insurance Experiment [@finkelstein2012], estimated using `SimpleLocalDistributionEstimator` (left) and `AdjustedLocalDistributionEstimator` (right). Health insurance coverage shifts the distribution of ED costs, with ML adjustment again yielding narrower confidence intervals.](oregon_ldte_costs_comparison.png)
 
 # Research Impact Statement
 
-The methods implemented in `dte_adj` have been published across machine learning and econometrics venues: ICML 2024 [@byambadalai2024estimatingdistributionaltreatmenteffects], Econometric Reviews [@oka2025regression], ICML 2025 [@byambadalai2025efficientestimationdistributionaltreatment], and NeurIPS 2025 [@byambadalai2025imperfectcompliance]. The package has been applied in industry settings, including analyzing the distributional impact of content promotion on user engagement at ABEMA, a major video streaming platform [@yasui2026abema]. The documentation includes tutorials demonstrating applications to the Hillstrom email marketing dataset (Figure 1) and the Oregon Health Insurance Experiment (Figure 2), facilitating adoption by researchers in economics, marketing, and healthcare.
+The methods implemented in `dte_adj` have been published across machine learning and econometrics venues: ICML 2024 [@byambadalai2024estimatingdistributionaltreatmenteffects], Econometric Reviews [@oka2025regression], ICML 2025 [@byambadalai2025efficientestimationdistributionaltreatment], and NeurIPS 2025 [@byambadalai2025imperfectcompliance]. The package has been applied in industry settings, including analyzing the distributional impact of content promotion on user engagement at ABEMA, a major video streaming platform [@yasui2026abema]. The documentation includes tutorials demonstrating applications to the Hillstrom email marketing dataset [@hillstrom2008] (Figure 1) and the Oregon Health Insurance Experiment [@finkelstein2012] (Figure 2), facilitating adoption by researchers in economics, marketing, and healthcare. End-to-end code for reproducing both figures is available in the online tutorials at <https://cyberagentailab.github.io/python-dte-adjustment/tutorials/>.
 
 # AI Usage Disclosure
 
-Generative AI tools (Claude) were used to assist with documentation writing and code review during development, and with copy-editing this paper. All AI-generated content was reviewed and validated by the human authors.
+Generative AI tools (Claude) were used to assist with documentation writing and code review during development, and to copy-edit this paper; the initial draft of the paper was written by the human authors without generative AI. All AI-generated content was reviewed and validated by the human authors.
 
 # Acknowledgements