[REVIEW]: Pycausal-Explorer: A Scikit-learn compatible causal inference toolkit

editorialbot commented 3 months ago

Submitting author: !--author-handle-->@gotolino@teonbrooks<!--end-editor-- Reviewers: @sara-02, @saeedahmadicp Archive: Pending

Status

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Markdown: [![status](https://joss.theoj.org/papers/78fb4501134f9fa186e48065bd490448/status.svg)](https://joss.theoj.org/papers/78fb4501134f9fa186e48065bd490448)

Reviewers and authors:

Please avoid lengthy details of difficulties in the review thread. Instead, please create a new issue in the target repository and link to those issues (especially acceptance-blockers) by leaving comments in the review thread below. (For completists: if the target issue tracker is also on GitHub, linking the review thread in the issue or vice versa will create corresponding breadcrumb trails in the link target.)

Reviewer instructions & questions

@sara-02 & @saeedahmadicp, your review will be checklist based. Each of you will have a separate checklist that you should update when carrying out your review. First of all you need to run this command in a separate comment to create the checklist:

@editorialbot generate my checklist

The reviewer guidelines are available here: https://joss.readthedocs.io/en/latest/reviewer_guidelines.html. Any questions/concerns please let @teonbrooks know.

✨ Please start on your review when you are able, and be sure to complete your review in the next six weeks, at the very latest ✨

Checklists

📝 Checklist for @saeedahmadicp

📝 Checklist for @sara-02

editorialbot commented 3 months ago

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editorialbot commented 3 months ago

Software report:

github.com/AlDanial/cloc v 1.90  T=0.04 s (1603.4 files/s, 177335.7 lines/s)
-------------------------------------------------------------------------------
Language                     files          blank        comment           code
-------------------------------------------------------------------------------
CSV                              2              0              0           3961
Python                          49            612            467           1582
reStructuredText                 5            129             75            188
Markdown                         4             40              0            163
YAML                             4             20             22            107
TeX                              1             12              0            100
TOML                             1              0              0             16
make                             1              4              6              9
CSS                              1              1              0              7
-------------------------------------------------------------------------------
SUM:                            68            818            570           6133
-------------------------------------------------------------------------------

Commit count by author:

    76  joao-vta
    75  Guilherme Goto Escudero
    18  heitormsantos
    11  gotolino
     2  Guilherme Escudero
     1  Igor Kuivjogi Fernandes

editorialbot commented 3 months ago

Paper file info:

📄 Wordcount for paper.md is 704

✅ The paper includes a Statement of need section

editorialbot commented 3 months ago

License info:

✅ License found: MIT License (Valid open source OSI approved license)

editorialbot commented 3 months ago

:point_right::page_facing_up: Download article proof :page_facing_up: View article proof on GitHub :page_facing_up: :point_left:

editorialbot commented 3 months ago

Reference check summary (note 'MISSING' DOIs are suggestions that need verification):

OK DOIs

- None

MISSING DOIs

- No DOI given, and none found for title: Scikit-learn: Machine Learning in Python
- 10.1214/18-aos1709 may be a valid DOI for title: Generalized random forests
- No DOI given, and none found for title: CausalML: Python Package for Causal Machine Learni...
- No DOI given, and none found for title: EconML: A Python Package for ML-Based Heterogeneou...
- No DOI given, and none found for title: DoWhy: A Python package for causal inference
- No DOI given, and none found for title: Metalearners for estimating heterogeneous treatmen...
- 10.2307/2669919 may be a valid DOI for title: Causal effects in nonexperimental studies: Reevalu...
- 10.1080/01621459.2017.1319839 may be a valid DOI for title: Estimation and inference of heterogeneous treatmen...
- 10.1093/biomet/asaa076 may be a valid DOI for title: Quasi-oracle estimation of heterogeneous treatment...
- 10.3386/w23564 may be a valid DOI for title: Double/debiased machine learning for treatment and...
- 10.1002/sim.8792 may be a valid DOI for title: Confounder selection strategies targeting stable t...
- No DOI given, and none found for title: Causal Explorer: A Causal Probabilistic Network Le...

INVALID DOIs

- None

saeedahmadicp commented 3 months ago

Review checklist for @saeedahmadicp

Conflict of interest

[x] I confirm that I have read the JOSS conflict of interest (COI) policy and that: I have no COIs with reviewing this work or that any perceived COIs have been waived by JOSS for the purpose of this review.

Code of Conduct

[x] I confirm that I read and will adhere to the JOSS code of conduct.

General checks

[x] Repository: Is the source code for this software available at the https://github.com/gotolino/pycausal-explorer?
[x] License: Does the repository contain a plain-text LICENSE or COPYING file with the contents of an OSI approved software license?
[ ] Contribution and authorship: Has the submitting author (@gotolino) made major contributions to the software? Does the full list of paper authors seem appropriate and complete?
[ ] Substantial scholarly effort: Does this submission meet the scope eligibility described in the JOSS guidelines
[x] Data sharing: If the paper contains original data, data are accessible to the reviewers. If the paper contains no original data, please check this item.
[x] Reproducibility: If the paper contains original results, results are entirely reproducible by reviewers. If the paper contains no original results, please check this item.
[x] Human and animal research: If the paper contains original data research on humans subjects or animals, does it comply with JOSS's human participants research policy and/or animal research policy? If the paper contains no such data, please check this item.

Functionality

[x] Installation: Does installation proceed as outlined in the documentation?
[x] Functionality: Have the functional claims of the software been confirmed?
[ ] Performance: If there are any performance claims of the software, have they been confirmed? (If there are no claims, please check off this item.)

Documentation

[x] A statement of need: Do the authors clearly state what problems the software is designed to solve and who the target audience is?
[x] Installation instructions: Is there a clearly-stated list of dependencies? Ideally these should be handled with an automated package management solution.
[ ] Example usage: Do the authors include examples of how to use the software (ideally to solve real-world analysis problems).
[x] Functionality documentation: Is the core functionality of the software documented to a satisfactory level (e.g., API method documentation)?
[x] Automated tests: Are there automated tests or manual steps described so that the functionality of the software can be verified?
[ ] Community guidelines: Are there clear guidelines for third parties wishing to 1) Contribute to the software 2) Report issues or problems with the software 3) Seek support

Software paper

[x] Summary: Has a clear description of the high-level functionality and purpose of the software for a diverse, non-specialist audience been provided?
[x] A statement of need: Does the paper have a section titled 'Statement of need' that clearly states what problems the software is designed to solve, who the target audience is, and its relation to other work?
[x] State of the field: Do the authors describe how this software compares to other commonly-used packages?
[ ] Quality of writing: Is the paper well written (i.e., it does not require editing for structure, language, or writing quality)?
[x] References: Is the list of references complete, and is everything cited appropriately that should be cited (e.g., papers, datasets, software)? Do references in the text use the proper citation syntax?

sara-02 commented 3 months ago

Review checklist for @sara-02

Conflict of interest

[x] I confirm that I have read the JOSS conflict of interest (COI) policy and that: I have no COIs with reviewing this work or that any perceived COIs have been waived by JOSS for the purpose of this review.

Code of Conduct

[x] I confirm that I read and will adhere to the JOSS code of conduct.

General checks

[x] Repository: Is the source code for this software available at the https://github.com/gotolino/pycausal-explorer?
[x] License: Does the repository contain a plain-text LICENSE or COPYING file with the contents of an OSI approved software license?
[x] Contribution and authorship: Has the submitting author (@gotolino) made major contributions to the software? Does the full list of paper authors seem appropriate and complete?
[x] Substantial scholarly effort: Does this submission meet the scope eligibility described in the JOSS guidelines
[ ] Data sharing: If the paper contains original data, data are accessible to the reviewers. If the paper contains no original data, please check this item.
[ ] Reproducibility: If the paper contains original results, results are entirely reproducible by reviewers. If the paper contains no original results, please check this item.
[x] Human and animal research: If the paper contains original data research on humans subjects or animals, does it comply with JOSS's human participants research policy and/or animal research policy? If the paper contains no such data, please check this item.

Functionality

[ ] Installation: Does installation proceed as outlined in the documentation?
[ ] Functionality: Have the functional claims of the software been confirmed?
[ ] Performance: If there are any performance claims of the software, have they been confirmed? (If there are no claims, please check off this item.)

Documentation

[ ] A statement of need: Do the authors clearly state what problems the software is designed to solve and who the target audience is?
[ ] Installation instructions: Is there a clearly-stated list of dependencies? Ideally these should be handled with an automated package management solution.
[ ] Example usage: Do the authors include examples of how to use the software (ideally to solve real-world analysis problems).
[ ] Functionality documentation: Is the core functionality of the software documented to a satisfactory level (e.g., API method documentation)?
[ ] Automated tests: Are there automated tests or manual steps described so that the functionality of the software can be verified?
[ ] Community guidelines: Are there clear guidelines for third parties wishing to 1) Contribute to the software 2) Report issues or problems with the software 3) Seek support

Software paper

[ ] Summary: Has a clear description of the high-level functionality and purpose of the software for a diverse, non-specialist audience been provided?
[ ] A statement of need: Does the paper have a section titled 'Statement of need' that clearly states what problems the software is designed to solve, who the target audience is, and its relation to other work?
[ ] State of the field: Do the authors describe how this software compares to other commonly-used packages?
[ ] Quality of writing: Is the paper well written (i.e., it does not require editing for structure, language, or writing quality)?
[ ] References: Is the list of references complete, and is everything cited appropriately that should be cited (e.g., papers, datasets, software)? Do references in the text use the proper citation syntax?

sara-02 commented 3 months ago

@editorialbot generate pdf

editorialbot commented 3 months ago

:point_right::page_facing_up: Download article proof :page_facing_up: View article proof on GitHub :page_facing_up: :point_left:

saeedahmadicp commented 2 months ago

Dear @teonbrooks, I have carefully reviewed the submission you sent. The article and the accompanying package aim to provide a Python module for causal inference and treatment effect estimation. While the Package is well-documented and contains example use cases and automated tests, as well as a well-written paper with all the necessary sections, there are a few issues that make the current version of the paper unsuitable for publication in the JOSS journal.

Firstly, the contribution of the package lacks novelty and sufficient need as there are already many similar packages available, such as Casual ML by Uber, which is open-sourced, regularly maintained, and widely used. Additionally, several other packages are available from Microsoft. The authors have failed to highlight any unique features of their package, making the scholarly impact of the paper insufficient.

Secondly, the authors haven't provided any guidelines for contributions to the repository. Additionally, there seems to be a conflict between the authors of the paper, as the fourth author in the GitHub contribution is different from the paper's authors i.e., Roseli de Deus Lopes.

[ ${\color{blue}Note}$ This is my first review in this journal, so let me know if I have missed anything @teonbrooks. ]

crvernon commented 1 month ago

@teonbrooks it looks like you have a question in waiting in this thread. Could you check this out when you get a minute? Thanks!

teonbrooks commented 1 month ago

@saeedahmadicp I'm very sorry for the delay. I have been a bit overwhelmed with work, my apologies. this review was very useful and I will definitely use this feedback when considering this article. thanks so much for your work on this.

@sara-02 do you have a a summary of your review or some comments you would like to share about your review.

I will take all these comments and provide my feedback on the article.

apologies again for the slow responses.

crvernon commented 4 weeks ago

:wave: @teonbrooks - please follow up with this one weekly to make sure things keep moving forward. Thanks!

crvernon commented 1 week ago

👋 @teonbrooks - just raising this one in your inbox...

👋 @teonbrooks - please follow up with this one weekly to make sure things keep moving forward. Thanks!

openjournals / joss-reviews