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[PRE REVIEW]: CausalTables.jl
: Simulating and storing data for statistical causal inference in Julia
#7535
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👋 @salbalkus - Please remove any formatting off the the title of your paper. This causes some issues in how our automated system handles things. Thanks. |
Hi @crvernon, thanks for letting me know -- I just removed the backticks from the title. Let me know if there are still any issues. |
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@crvernon If you agree I'll take this one up ? Although I do not really understand hte current error message, only the topic ticked my eye ;) |
@editorialbot assign @lrnv as editor All yours! Thank you! |
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Five most similar historical JOSS papers: CRE: An R package for interpretable discovery and inference of heterogeneous treatment effects medoutcon: Nonparametric efficient causal mediation analysis with machine learning in R tehtuner: An R package to fit and tune models for the conditional average treatment effect CVtreeMLE: Efficient Estimation of Mixed Exposures using Data Adaptive Decision Trees and Cross-Validated Targeted Maximum Likelihood Estimation in R txshift: Efficient estimation of the causal effects of stochastic interventions in R |
@salbalkus Thanks. Greatings btw, I'll be your editor on this submission then. Our first task is to find two-to-three suitable reviewers for this work. Would you by chance be able to propose a few ones ? |
@lrnv Absolutely. It's unfortunate that the editorialbot detected all R packages; I think blind-contours would be a good reviewer for the topic if they know Julia. olivierlabayle may be interested in reviewing since this paper cites TMLE.jl. Otherwise, looking through the JOSS database, some potential reviewers include juliohm, epiben, and gdalle. |
@salbalkus I removed @ s from your message to avoid pinging everyone. Let me do that instead, thanks for the suggestions. |
I can find some time to review until the end of the year. Would that be ok?
Em dom., 8 de dez. de 2024, 16:55, Salvador Balkus ***@***.***>
escreveu:
… @lrnv <https://github.com/lrnv> Absolutely. It's unfortunate that the
editorialbot detected all R packages; I think @blind-contours
<https://github.com/blind-contours> would be a good reviewer for the
topic if they know Julia.
@olivierlabayle <https://github.com/olivierlabayle> may be interested in
reviewing since this paper cites TMLE.jl.
Otherwise, looking through the JOSS database, some potential reviewers
include @juliohm <https://github.com/juliohm>, @epiben
<https://github.com/epiben>, and @gdalle <https://github.com/gdalle>.
Would any of you be interested in reviewing this submission?
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I am happy to review |
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@juliohm @olivierlabayle @salbalkus Okay then, let us close this thread and start up the review process in the next one. |
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OK, I've started the review over in #7580. |
Submitting author: @salbalkus (Salvador Balkus)
Repository: https://github.com/salbalkus/CausalTables.jl
Branch with paper.md (empty if default branch): main
Version: v1.1.1
Editor: @lrnv
Reviewers: @juliohm, @olivierlabayle
Managing EiC: Chris Vernon
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