Causal Inference

Description from DukeHub

Statistical issues in causality and methods for estimating causal effects. Randomized designs and alternative designs and methods for when randomization is infeasible: matching methods, propensity scores, longitudinal treatments, regression discontinuity, instrumental variables, and principal stratification. Methods are motivated by examples from social sciences, policy and health sciences. Prerequisite: Statistical 521L or 721.

Skills

• data and statisticsdescription: "statistical" and 3 more

AI-resilient: 1 of 5 1/5

• No: Physical, lab or field work
• No: Live work with people
• ✓Yes: Judgment someone is accountable for · "policy"
• 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

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