Causal Inference for Health Data

4 units · Undergraduate upper division (100-199)

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.