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Spring 2026
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5641. Causal Inference and Measurement for Data Science

2.00 credits

Prerequisites: Open to students enrolled in the M.S. Data Science program or with instructor consent. Recommended preparation: Knowledge of Introductory inferential and descriptive statistics.

Grading Basis: Graded

Principles and practice of causal inference and measurement for data scientists. Topics include: fundamentals of causal inference, establishing causal (rather than correlational) relationships -- via AB tests, controlling for confounders, and the use of panel data -- and the fundamentals of measurement. Validity and reproducibility are themes throughout the course.


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