Analytic Methods IV: Non-randomized Designs

Description from DukeHub

This course is the second in a 2-semester series covering non-randomized study designs that rely on advanced regression models. Students will learn how to use non-randomized designs, such as difference-in-differences, to support causal inference. Students will use statistical software to estimate and interpret regression-based treatment effects. The course will culminate in a term paper where students will independently use observational data to address a research question. At the end of this course, students will understand key assumptions, strengths, and limitations of non-randomized designs and apply these tools to answer population health research questions. Recommended prerequisite: POPHS 903.

Skills

• data and statisticsdescription: "regression" and 3 more
• research methodsAI review: "independently use observational data to address a research question"

AI-resilient: 0 of 5 0/5

• No: Physical, lab or field work
• No: Live work with people
• No: Judgment someone is accountable for
• No: A craft you practice with your hands or body
• No: Small, in-person format

The checks lean on Microsoft Research, Working with AI (2025), which found generative AI applies least to physical work and hands-on work with people. A signal, not a promise. How the score works

More in POPHS

• Regression Methods for Population Health POPHS 702
• Introduction to Statistical Programming for Population Health Sciences II POPHS 704
• Introduction to the US Healthcare System POPHS 706
• Research Methods II: Evidence, Methods, and Decision Making in Population Health POPHS 708
• Professional Development II POPHS 710
• Population Health Capstone POPHS 752

All POPHS courses · All of Graduate School