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

References

• Angrist, J. D., and Pischke, J.-S. (2014), Mastering’Metrics: The Path From Cause to Effect, Princeton, NJ: Princeton University Press.
   Google Scholar
• Barrett, M. (2019a), “Common Structures of Bias,” available at https://ggdag.netlify.com/articles/bias-structures.html.
   Google Scholar
• Barrett, M. (2019b), “ggdag: Analyze and Create Elegant Directed Acyclic Graphs,” R Package Version 0.2.0, available at https://CRAN.R-project.org/package=ggdag.
   Google Scholar
• Berkson, J. (1946), “Limitations of the Application of Fourfold Table Analysis to Hospital Data,” Biometrics Bulletin, 2, 47–53. DOI: 10.2307/3002000.
   Google Scholar
• Cobb, G. W. (2015), “Mere Renovation Is Too Little Too Late: We Need to Rethink Our Undergraduate Curriculum From the Ground Up,” The American Statistician, 69, 266–282, DOI: 10.1080/00031305.2015.1093029.
   Web of Science ®Google Scholar
• Cornfield, J., Haenszel, W., Hammond, E. C., Lilienfeld, A. M., Shimkin, M. B., and Wynder, E. L. (1959), “Smoking and Lung Cancer: Recent Evidence and a Discussion of Some Questions,” Journal of the National Cancer Institute, 22, 173–203.
   PubMed Web of Science ®Google Scholar
• Cummiskey, K. (2019), “Causal Inference in Introductory Statistics Courses,” available at https://github.com/kfcaby/causalLab.
   Google Scholar
• Cummiskey, K., Adams, B., Pleuss, J., Turner, D., Clark, N., and Watts, K. (2020), “Causal Inference in Introductory Statistics Courses,” Journal of Statistics Education, DOI: 10.1080/10691898.2020.1713936.
   Web of Science ®Google Scholar
• Elwert, F. (2013), “Graphical Causal Models,” in Handbook of Causal Analysis for Social Research, ed. S. Morgan, Dordrecht: Springer, pp. 245–273.
   Google Scholar
• Gelman, A., and Hill, J. (2012), Data Analysis Using Regression and Multilevel/Hierarchical Models, New York: Cambridge University Press.
   Google Scholar
• Gould, R. (2017), “Data Literacy Is Statistical Literacy,” Statistics Education Research Journal, 16, 22–25.
   Google Scholar
• Hardin, J., Hoerl, R., Horton, N. J., Nolan, D., Baumer, B., Hall-Holt, O., Murrell, P., Peng, R., Roback, P., Lang, D. T., and Ward, M. D. (2015), “Data Science in Statistics Curricula: Preparing Students to ‘Think With Data’,” The American Statistician, 69, 343–353, DOI: 10.1080/00031305.2015.1077729.
   Web of Science ®Google Scholar
• Hernán, M. A., Hsu, J., and Healy, B. (2019), “A Second Chance to Get Causal Inference Right: A Classification of Data Science Tasks,” CHANCE, 32, 42–49, DOI: 10.1080/09332480.2019.1579578.
   Google Scholar
• Holland, P. W. (1986), “Statistics and Causal Inference,” Journal of the American statistical Association, 81, 945–960. DOI: 10.1080/01621459.1986.10478354.
   Web of Science ®Google Scholar
• Imbens, G. W., and Rubin, D. B. (2015), Causal Inference in Statistics, Social, and Biomedical Sciences, Cambridge: Cambridge University Press.
   Google Scholar
• Jamie, D. M. (2002), “Using Computer Simulation Methods to Teach Statistics: A Review of the Literature,” Journal of Statistics Education, 10, 1–20. DOI: 10.1080/10691898.2002.11910548.
   Google Scholar
• Kaplan, D. (2011), Statistical Modeling: A Fresh Approach (2nd ed.), Project MOSAIC Books. Retrieved from http://project-mosaic-books.com/
   Google Scholar
• Kaplan, D. (2018), “Teaching Stats for Data Science,” The American Statistician, 72, 89–96, DOI: 10.1080/00031305.2017.1398107.
   Web of Science ®Google Scholar
• Lindeløv, J. K. (2019), “Common Statistical Tests Are Linear Models (or: How to Teach Stats),” available at https://lindeloev.github.io/tests-as-linear/.
   Google Scholar
• Morgan, S. L., and Winship, C. (2015), Counterfactuals and Causal Inference (2nd ed.), Cambridge: Cambridge University Press.
   Google Scholar
• National Academies of Sciences, Engineering, and Medicine (2018), Data Science for Undergraduates: Opportunities and Options, Washington, DC: The National Academies Press, DOI: 10.17226/25104.
   Google Scholar
• Pearl, J. (2013), “Linear Models: A Useful ‘Microscope’ for Causal Analysis,” Journal of Causal Inference, 1, 155–170. DOI: 10.1515/jci-2013-0003.
   Google Scholar
• Pearl, J. (2019), “The Seven Tools of Causal Inference, With Reflections on Machine Learning,” Communications of the ACM, 62, 54–60.
   Web of Science ®Google Scholar
• Pearl, J., Glymour, M., and Jewell, N. P. (2016), Causal Inference in Statistics: A Primer, Chichester: Wiley.
   Google Scholar
• Pearl, J., and Mackenzie, D. (2018), The Book of Why: The New Science of Cause and Effect, New York: Basic Books.
   Google Scholar
• Peters, J., Janzing, D., and Schölkopf, B. (2017), Elements of Causal Inference: Foundations and Learning Algorithms, Cambridge, MA: MIT Press.
   Google Scholar
• Pruim, R., Kaplan, D., and Horton, N. (2017), “The Mosaic Package: Helping Students to ‘Think With Data’ Using R,” The R Journal, 9, 77–102, DOI: 10.32614/RJ-2017-024.
   Google Scholar
• R Core Team (2019), R: A Language and Environment for Statistical Computing, Vienna, Austria: R Foundation for Statistical Computing, available at https://www.R-project.org/.
   Google Scholar
• Ridgway, J. (2016), “Implications of the Data Revolution for Statistics Education,” International Statistical Review, 84, 528–549, DOI: 10.1111/insr.12110.
   Web of Science ®Google Scholar
• Rossman, A., and De Veaux, R. (2016), “Interview With Richard De Veaux,” Journal of Statistics Education, 24, 157–168, DOI: 10.1080/10691898.2016.1263493.
   Web of Science ®Google Scholar
• Rossman, A. J., and Witmer, J. (2019), “Interview With Jeff Witmer,” Journal of Statistics Education, 27, 48–57, DOI: 10.1080/10691898.2019.1603506.
   Web of Science ®Google Scholar
• Schield, M. (2018), “Confounding and Cornfield: Back to the Future,” in Looking Back, Looking Forward. Proceedings of the Tenth International Conference on Teaching Statistics, eds. M. A. Sorto, A. White, and L. Guyot, available at http://www.statlit.org/pdf/2018-Schield-ICOTS.pdf.
   Google Scholar
• Schloerke, B., Allaire, J. J., and Borges, B. (2019), “learnr: Interactive Tutorials for R,” R Package Version 0.10.0, available at https://CRAN.R-project.org/package=learnr.
   Google Scholar
• Simpson, E. H. (1951), “The Interpretation of Interaction in Contingency Tables,” Journal of the Royal Statistical Society, Series B, 13, 238–241. DOI: 10.1111/j.2517-6161.1951.tb00088.x.
   Google Scholar
• Stark, P. B., and Saltelli, A. (2018), “Cargo-Cult Statistics and Scientific Crisis,” Significance, 15, 40–43, DOI: 10.1111/j.1740-9713.2018.01174.x.
   Google Scholar
• Steel, E. A., Liermann, M., and Guttorp, P. (2019), “Beyond Calculations: A Course in Statistical Thinking,” The American Statistician, 73, 392–401, DOI: 10.1080/00031305.2018.1505657.
   Web of Science ®Google Scholar
• Wild, C. J., Pfannkuch, M., Regan, M., and Parsonage, R. (2017), “Accessible Conceptions of Statistical Inference: Pulling Ourselves Up by the Bootstraps,” International Statistical Review, 85, 84–107, DOI: 10.1111/insr.12117.
   Web of Science ®Google Scholar
• Wood, B. L., Mocko, M., Everson, M., Horton, N. J., and Velleman, P. (2018), “Updated Guidelines, Updated Curriculum: The Gaise College Report and Introductory Statistics for the Modern Student,” CHANCE, 31, 53–59, DOI: 10.1080/09332480.2018.1467642.
   Google Scholar