Teaching Causal Diagrams in an Introductory Statistics Course and Beyond

Music City Center 

There's no shortage of material in introductory statistics courses, and the choice of topics is often out of a teacher's control, determined by tradition and institutional expectations. So why consider adding one more? Tools from Causal Inference offer an alluring promise: the ability to estimate cause-and-effect relationships from observational data—a skill increasingly valuable in both academic and commercial settings.

There are multiple frameworks within Causal Inference, and even experts disagree on which is best. Rather than advocating for a single approach, I focus on core concepts that provide a flexible foundation—one that prepares students to engage with the advanced frameworks they may encounter later. I'll share my experience incorporating Causal Diagrams—Directed Acyclic Graphs, as formalized by Judea Pearl—into an introductory statistics course. This visual tool helps students distinguish between types of correlation while emphasizing that conclusions depend on assumptions as well as data.

In our AI-driven era, a foundation in causal reasoning is critical for understanding the limits of predictive models. The proposed module fits within one week (3 lecture hours, 6 homework hours) and can be integrated into existing sections on observational studies or descriptive statistics. It requires minimal technical background and lays the groundwork for further study in either traditional statistical methods or AI-driven causal discovery. Participants will come away with practical strategies for introducing causal thinking in a way that complements traditional topics and equips students with tools essential for the future.

Causal inference