More than averages: using causal quartets to illustrate variability
Article Title: Causal Quartets: Different Ways to Attain the Same Average Treatment Effects [DOI:10.1080/00031305.2023.2267597]Authors & Year: A. Gelman, J. Hullman, and L. Kennedy (2023)Journal: The American StatisticianReview Prepared by Peter A. Gao Causal inference research commonly focuses on estimation of average treatment effects: In a target population, what is the difference in mean outcomes between individuals who receive the treatment and individuals who receive a control? For example, imagine an experiment investigating whether limiting daily phone usage improves academic performance among high school students. Subjects are randomly sorted into a treatment group (limited to one hour of phone time daily) or a control group (unrestricted) and over the course of a semester, their academic performance is measured using exams. In this case, the average treatment effect is simply the average exam score of the treated students minus the average score of the control students. If this effect is large and…