Causal Inference in Introductory Statistics Courses. Issue 1 (2nd January 2020)

• Record Type: Journal Article
• Authors: Cummiskey, Kevin; Adams, Bryan; Pleuss, James; Turner, Dusty; Clark, Nicholas; Watts, Krista
• Is Part Of: Journal of statistics education. Volume 28:Issue 1(2020)
• Page Start: 2
• Page End: 8
• Publication Date: 2020-01-02
• Subjects: Causal DAG -- Causal diagrams -- Confounding -- Multivariable thinking -- Student activity; Statistics -- Study and teaching -- Periodicals; Statistics -- Study and teaching; Periodicals; 519.5071
• DOI: 10.1080/10691898.2020.1713936
• Languages: English
• ISSNs: 1069-1898

Abstract

Abstract: Over the last two decades, statistics educators have made important changes to introductory courses. Current guidelines emphasize developing statistical thinking in students and exposing them to the entire investigative process in the context of interesting research questions and real data. As a result, many concepts (confounding, multivariable models, study design, etc.) previously reserved only for higher-level courses now appear in introductory courses. Despite these changes, causality is rarely discussed in introductory courses, except for warning students "correlation does not imply causation" or covering the special case of randomized controlled experiments. In this article, we argue causal inference concepts align well with statistics education guidelines for introductory courses by developing statistical and multivariable thinking, exposing students to many aspects of the investigative process, and fostering active learning. We discuss how to integrate causal inference concepts into introductory courses using causal diagrams and provide an illustrative example with youth smoking data. Through our website, we also provide a guided student activity and instructor resources. Supplementary materials for this article are available online.