We present an instructional approach to teaching causal inference using Bayesian networks and "do"-Calculus, which requires less prerequisite knowledge of statistics than existing approaches and can be consistently implemented in beginner to advanced levels courses. Moreover, this approach aims to address the central question in causal…

Spurred by recent writings regarding statistical pragmatism, we propose a simple, practical approach to introducing students to a new style of statistical thinking that models nature through the lens of data-generating processes, not populations. (Contains 5 figures.)