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Journal of Statistics Education Volume 28, 2020 - Issue 2
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Articles

Why We Should Teach Causal Inference: Examples in Linear Regression With Simulated Data

Karsten LübkeInstitute for Empirical Research & Statistics, FOM University of Applied Sciences, Dortmund, Germany; Correspondence[email protected]
,
Matthias GehrkeInstitute for Empirical Research & Statistics, FOM University of Applied Sciences, Frankfurt am Main, Germany;
,
Jörg HorstFaculty of Engineering and Mathematics, Bielefeld University of Applied Sciences, Bielefeld, Germany;
&
Gero SzepannekSchool of Business Studies, Hochschule Stralsund – University of Applied Sciences, Stralsund, Germany
Pages 133-139 | Published online: 13 May 2020

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Yonggang Lu, Qiujie Zheng & Daniel Quinn. (2023) Introducing Causal Inference Using Bayesian Networks and do-Calculus. Journal of Statistics and Data Science Education 31:1, pages 3-17.
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Alex Reinhart, Ciaran Evans, Amanda Luby, Josue Orellana, Mikaela Meyer, Jerzy Wieczorek, Peter Elliott, Philipp Burckhardt & Rebecca Nugent. (2022) Think-Aloud Interviews: A Tool for Exploring Student Statistical Reasoning. Journal of Statistics and Data Science Education 30:2, pages 100-113.
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Michael Bücker, Gero Szepannek, Alicja Gosiewska & Przemyslaw Biecek. (2022) Transparency, auditability, and explainability of machine learning models in credit scoring. Journal of the Operational Research Society 73:1, pages 70-90.
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George T. H. Ellison. (2021) Might Temporal Logic Improve the Specification of Directed Acyclic Graphs (DAGs)?. Journal of Statistics and Data Science Education 29:2, pages 202-213.
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Articles from other publishers (12)

Samuel Kilian, Katrin Meisenbacher & Moritz S. Bischoff. (2023) Ereigniszeitanalyse und die Beurteilung diagnostischer VerfahrenTime to event analysis and the assessment of diagnostic procedures. Gefässchirurgie 28:8, pages 599-610.
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Kirsty Kitto, Ben Hicks & Simon Buckingham Shum. (2023) Using causal models to bridge the divide between big data and educational theory. British Journal of Educational Technology 54:5, pages 1095-1124.
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Kevin Cummiskey & Karsten Lübke. (2022) Causality in statistics and data science education. AStA Wirtschafts- und Sozialstatistisches Archiv 16:3-4, pages 277-286.
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Kyle A. Kurkela, Rose A. Cooper, Ehri Ryu & Maureen Ritchey. (2022) Integrating Region- and Network-level Contributions to Episodic Recollection Using Multilevel Structural Equation Modeling. Journal of Cognitive Neuroscience 34:12, pages 2341-2359.
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Xuanhui Zhang, Weijia Zhang, Yuxiang (Chris) Zhao & Qinghua Zhu. (2022) Imbalanced volunteer engagement in cultural heritage crowdsourcing: a task-related exploration based on causal inference. Information Processing & Management 59:5, pages 103027.
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Andrew Forney & Scott Mueller. (2022) Causal inference in AI education: A primer. Journal of Causal Inference 10:1, pages 141-173.
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Karsten Lübke & Bianca Krol. 2022. Quantitative Forschung in Masterarbeiten. Quantitative Forschung in Masterarbeiten 499 509 .
Silvia Boßow-Thies & Oliver Gansser. 2022. Quantitative Forschung in Masterarbeiten. Quantitative Forschung in Masterarbeiten 1 30 .
Huaqing Jiang & Xiaohui Yang. (2021) Comparison between traditional model and causal intervention model: A case study baesd on directed acyclic graph. Comparison between traditional model and causal intervention model: A case study baesd on directed acyclic graph.
Gero Szepannek & Karsten Lübke. (2021) Facing the Challenges of Developing Fair Risk Scoring Models. Frontiers in Artificial Intelligence 4.
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Matthias Gehrke, Tanja Kistler, Karsten Lübke, Norman Markgraf, Bianca Krol & Sebastian Sauer. (2021) Statistics education from a data‐centric perspective. Teaching Statistics 43:S1.
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Theiss Bendixen. (2020) How cultural evolution can inform the science of science communication—and vice versa. Humanities and Social Sciences Communications 7:1.
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