Introduction to Causal Inference

Description from DukeHub

Methods for causal inference, including confounding and selection bias in observational or quasi-experimental research designs, propensity score methodology, instrumental variables, and methods for non-compliance in randomized clinical trials. Prerequisite(s): BIOSTAT 701/701A or permission of the director of graduate studies. Credits: 3

Skills

• data and statisticstitle: "causal inference"
• research methodsdescription: "research designs" and 1 more

AI-resilient: 1 of 5 1/5

• No: Physical, lab or field work
• ✓Yes: Live work with people · "clinical"
• 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 BIOSTAT

• Introduction to Statistical Theory and Methods II BIOSTAT 704
• Applied Biostatistical Methods II BIOSTAT 705
• Introduction to the Practice of Biostatistics II BIOSTAT 706
• Clinical Trial Design and Analysis BIOSTAT 708
• Analysis of Correlated and Longitudinal Data BIOSTAT 718
• Introduction to Statistical Programming II (SAS) BIOSTAT 722

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