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- Add "(RCTs, also known as A/B tests)" clarification per reviewer feedback - Add DoWhy and EconML comparison in Statement of Need section - Add imperfect compliance paper reference for LDTE estimators - Update CAR paper to ICML'25 proceedings format with updated author list - Update multi-task learning paper author list (add Shunsuke Uto) - Fix Fisher citation key to match bibliography 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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Pull request overview
Adds a draft JOSS manuscript for the dte_adj package, documenting its motivation, core estimators/methods, and related literature for submission.
Changes:
- Introduces
paper.mdwith JOSS front-matter, summary/need statements, and a feature overview (including linked figures). - Adds
paper.bibwith bibliography entries referenced from the manuscript.
Reviewed changes
Copilot reviewed 2 out of 2 changed files in this pull request and generated 2 comments.
| File | Description |
|---|---|
| paper.md | New JOSS paper draft (front-matter + narrative + figures) describing dte_adj and its capabilities. |
| paper.bib | New bibliography backing citations used in paper.md. |
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- Add required "State of the Field" section comparing with SciPy, DoWhy, EconML, and causal-curve packages - Add required "Software Design" section explaining class architecture - Add required "Research Impact Statement" with ICML publications and industry usage - Add required "AI Usage Disclosure" section - Remove detailed "Features" section (API docs belong in documentation) - Add SciPy and causal-curve references to bibliography - Ensure word count is within 750-1750 range (776 words) - Add VanderPlas to typos allowlist (author name in SciPy citation) References: - JOSS submission guidelines: https://joss.readthedocs.io/en/latest/paper.html - JOSS review checklist: https://github.com/openjournals/joss/blob/main/docs/review_checklist.md 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Fix author metadata: make Tomu Hirata the single corresponding author and remove conflicting equal-contrib flag - Fix NumPy citation: replace Unicode en-dash (–) with BibTeX format (--) for compatibility across citation pipelines 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Move paper.md and paper.bib into paper/ to avoid top-level clutter - Add hillstorm_dte.png and oregon_ldte_costs_comparison.png from tutorials - Add figure captions showing simple vs ML-adjusted comparison for both the Hillstrom email campaign and Oregon Health Insurance Experiment - Cross-reference figures in Research Impact Statement - Add dataset citations (Hillstrom 2008, Finkelstein et al. 2012) - Add GitHub Action (draft-pdf.yml) to auto-build JOSS PDF on push using paper-path: paper/paper.md Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
![github-advanced-security[bot]](https://avatars.githubusercontent.com/in/57789?s=60&v=4){kind=small}
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Addresses CodeQL security alert: restrict GITHUB_TOKEN to read-only contents access following least-privilege principle. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
inara/pandoc runs from the repo root, so relative image paths in paper/paper.md resolve against root, not paper/. Copy PNGs to root before the build step so pandoc finds them. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Pandoc extracts \label{} from caption text and also auto-generates a
figure label, causing the label to be defined twice. Remove explicit
\label{} from captions and replace \autoref{} with plain text references.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
inara CDs into the paper/ directory before running pandoc, so images in paper/ are already on the resource path. No copying needed. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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I’ve added some brief comments regarding the citations.
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| # Research Impact Statement | ||
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| The methods implemented in `dte_adj` have been published at top machine learning venues: ICML 2024 [@byambadalai2024estimatingdistributionaltreatmenteffects] and ICML 2025 [@byambadalai2025efficientestimationdistributionaltreatment]. The package has been used internally at CyberAgent, Inc. for analyzing A/B tests where distributional impacts are critical, such as evaluating interventions on user engagement metrics where tail behavior matters more than averages. 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. |
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The package has been used internally at CyberAgent, Inc. for analyzing A/B tests where distributional impacts are critical, such as evaluating interventions on user engagement metrics where tail behavior matters more than averages.
How about citing the following paper in this section?
https://link.springer.com/article/10.1007/s42973-026-00234-y
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The methods implemented in
dte_adjhave been published at top machine learning venues: ICML 2024 [@byambadalai2024estimatingdistributionaltreatmenteffects] and ICML 2025 [@byambadalai2025efficientestimationdistributionaltreatment].
How about citing these papers to demonstrate that the approach is widely accepted in both the AI and economics communities?
https://www.tandfonline.com/doi/full/10.1080/07474938.2025.2538843
https://neurips.cc/virtual/2025/loc/san-diego/poster/119035
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Added JOSS paper draft and github action to compile the markdown paper into pdf