Refining Students' Statistical Inference, Modeling, Visualization, and Computation Skills through the Lens of Causality

Music City Center 

Although undergraduate students know that "correlation doesn't imply causation" and that "confounding variables" pose problems, they usually don't know what does imply causation, or how to diagnose when confounding may be a concern. In this talk, I give guidance on how instructors can improve students' understanding of causality and confounding. Even if your expertise is not in causal inference, I show how you can teach causality to facilitate spiral learning in a statistics curriculum, where students practice many statistical skills (inference, modeling, visualization, computation) through the lens of causality. For example, students can establish many important theoretical results for causality with expectations, variances, and covariances; these results in turn motivate modeling tasks, perhaps with linear regression, logistic regression, or more advanced machine learning. A core idea is that measuring causal effects boils down to "apples-to-apples" comparisons, which can be diagnosed with visualizations and created by computationally matching similar treatment and control subjects, which is intuitive for students. These insights are based on courses I've developed at Carnegie Mellon University, including a sophomore-level undergraduate class on causality and education programs for working professionals outside of statistics. Thus, attendees of this talk will walk away with a set of tools to teach causality to improve undergraduate statistics skills, either in a standalone class or a couple of lessons.

Causal inference

confounding