STATS C160

Causal Inference for Health Data

Statistics · 4 units · Undergraduate upper division (100-199)

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Recommended: course 102A is recommended but not required, especially for students with high proficiency in python. 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.

Requisites

Official UCLA wording
Requisites: course 20 or 21 or Program in Computing 16A, and courses 100A, 101A. Preparation: basics of probability theory and regression modelling.

BruinTree reads · Prerequisite

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Requires

Everything that has to come before this course, not just the courses named in the requisite above.

STATS C160

STATS 20Introduction to Statistical Programming with R
- STATS 10Introduction to Statistical Reasoning
- STATS 12Introduction to Statistical Methods for Geography and Environmental Studies
- ECON 41Probability and Statistics for Economists
  - MATH 31ADifferential and Integral Calculus1 more beneath

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No course in the catalog lists this as a requisite.