STATS C160
Causal Inference for Health Data
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From the registrar's catalog
Lecture, three hours. Requisites: course 20 or 21 or Program in Computing 16A, and courses 100A, 101A. Recommended: course 102A is recommended but not required, especially for students with high proficiency in python. Preparation: basics of probability theory and regression modelling. Covers the foundations of the graphical approach to causal inference, with a specific focus on applications to health data, analyzing a range of real-world datasets. Concurrently scheduled with course C260. P/NP or letter grading.
Sections in Spring 2027
From the people who took it
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