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Activity Number: 195 - SPEED: Modernization of What, How, and Where We Teach Statistics Part 2
Type: Contributed
Date/Time: Monday, July 29, 2019 : 11:35 AM to 12:20 PM
Sponsor: Section on Statistics and Data Science Education
Abstract #307560
Title: Causal Inference in Introductory Statistics Courses
Author(s): Kevin Cummiskey* and Bryan Adams and James Pleuss and Dusty Turner and Nicholas Clark and Krista Watts
Companies: West Point and West Point and West Point and West Point and West Point and West Point
Keywords: Causal Inference; Introductory Statistics; Causal Diagrams; Student Activity

In the last two decades, statistics educators 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 paper, we argue causal inference concepts align well with statistics education guidelines for introductory courses by developing statistical and multivariable thinking, exposing students to the entire 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 teenage smoking data. Through our website, we also provide a guided student activity and instructor resources.

Authors who are presenting talks have a * after their name.

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