Causality in statistics and data science education
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DOI: 10.1007/s11943-022-00311-9
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References listed on IDEAS
- Andrew Gelman & Aki Vehtari, 2021. "What are the Most Important Statistical Ideas of the Past 50 Years?," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(536), pages 2087-2097, October.
- Jessica Utts, 2021. "Enhancing Data Science Ethics Through Statistical Education and Practice," International Statistical Review, International Statistical Institute, vol. 89(1), pages 1-17, April.
- Daniel Kaplan, 2018. "Teaching Stats for Data Science," The American Statistician, Taylor & Francis Journals, vol. 72(1), pages 89-96, January.
- Jim Ridgway, 2016. "Implications of the Data Revolution for Statistics Education," International Statistical Review, International Statistical Institute, vol. 84(3), pages 528-549, December.
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Cited by:
- Timo Schmid & Markus Zwick, 2022. "Editorial," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 16(3), pages 167-170, December.
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Keywords
Statistics education research; Data Science; Causality; Bias and Confounding;All these keywords.
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