April Issue Focuses on Causal Inference, Computing, Technology
Nicholas Horton
The April issue of the Journal of Statistics and Data Science Education is freely available online. The issue includes an editorial, plus 10 articles on a variety of topics and projects.
The editorial focuses on teaching causal inference, which is also the subject of the first paper in the issue. Yonggang Lu, Qiujie Zheng, and Daniel Quinn introduce causal inference using Bayesian networks and do-Calculus.
A variety of causal DAGs, as seen in “Introducing Causal Inference Using Bayesian Networks and do-Calculus”
A cluster of papers about computing and technology also feature in this issue, including the following:
- “Teaching Statistics and Data Analysis with R” by Mary C. Tucker, Stacy T. Shaw, Ji Y. Son, and James W. Stigler
- “Teaching Monte Carlo Simulation with Python” by Justin O. Holman and Allie Hacherl
- “SCRATCH to R: Toward an Inclusive Pedagogy in Teaching Coding” by Shu-Min Liao
- “Open-Source Tools for Training Resources” by Candace Savonen, Carrie Wright, Ava M. Hoffman, John Muschelli, Katherine Cox, Frederick J. Tan, and Jeffrey T. Leek.
The issue rounds out with papers about medical statistics (Benjamin Mayer, Anja Kuemmel, Marianne Meule, and Rainer Muche), a learning intervention to promote self-efficacy (D. Jake Follmer), results from converting a biostatistics course to flipped and online formats (Brandon J. George and Juan Leon), and a study of the effects of anxiety and attitudes on exam scores (Kelly Rhea MacArthur and Jonathan B. Santo).
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