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Glass-Box Modeling Working Group

The Glass-Box Modeling Working Group is a small collective interested in glass-box models: those expressed as a linear combination of a parsimonious set of meaningful parameters with quantified uncertainty, prioritizing transparency and honest interpretability alongside predictive performance.

The group stewards Data Diction, where we write about data and the stories they tell more freely than we can in academic journals. Every post is archived with a citable DOI through Rogue Scholar. If you are interested in contributing a post or collaborating on a piece, feel free to leave a comment on an existing post, or get in touch!

Date Post DOI
Apr 2026 Can an AI assistant handle the tedious parts of academic writing? 10.59350/vj2m3-s2t36
Dec 2025 How can I guarantee a significant result? 10.59350/6mzf8-xzd69
Dec 2025 Does debiasing estimates lead to better predictions? 10.59350/m6cm8-x2656
Nov 2025 What do we mean by glass-box, exactly? 10.59350/9dhes-thd51
Jul 2023 Did Denver’s 2022 ‘Zero Fare for Cleaner Air’ campaign actually work? 10.59350/sktbs-b7v46
Jun 2023 Detecting interactions in R 10.59350/f11j1-neh89
Jun 2022 Welcome to Data Diction 10.59350/xmjb6-qna36

StatCTR

The Statistical Consulting Training Repository includes indexed, vetted videos and online resources. I collaborated with Julia Sharp and Emily Griffith to build this resource and have recently integrated it within the American Statistical Association’s Statistical Consulting Section.

 

Last updated: June 2026