Week 10 - Causal Diagrams & Assessing Studies
Overview
This week has two connected parts. Tuesday: we introduce causal diagrams (DAGs) — a visual framework for thinking about causality, confounding, and what to control for. Thursday: we apply that framework to assessing regression validity (internal and external validity, omitted variable bias, measurement error, simultaneity).
Reading Guide
Tuesday: Causal Diagrams (DAGs)
Read the following chapters from Huntington-Klein, The Effect (free online):
The Effect, Chapter 6: Causal Diagrams
What are DAGs? Nodes, arrows, and how to represent a data generating process visually.
The Effect, Chapter 7: Drawing Causal Diagrams
How to build a DAG for your own research question. Practical guidance on simplifying and avoiding common mistakes.
The Effect, Chapter 8: Causal Paths and Closing Back Doors
The key chapter: front door vs. back door paths, open vs. closed paths, confounders, colliders, and the backdoor criterion.
Thursday: Assessing Regression Validity (SW Chapter 9)
SW 9.1 Internal and External Validity
Get those definitions, and threats to external validity
SW 9.2 Threats to Internal Validity of Multiple Regression Analysis
Five big threats. And how to handle them.
Bonus: SW 11.1 Linear Probability Models
Optional background if you want a bit more on [linear probability models]{.kw}. This is not part of Chapter 9, but it connects directly to Thursday’s class and Lab 6.
SW 9.4 Example: Test Score and Class Size
No new content, but this is a really good walk-through of the 9.1 and 9.2 content.
