CHS 787 - Causal Inference Methods in Public Health

(3 units)

This course covers statistical methods used for causal inference in public health and biomedical research. In this course, students will learn how causal effects are defined, what assumptions about data and models are necessary, and how to implement and interpret various causal inference statistical methods. The R statistical programming language will be used throughout this course.

Maximum units a student may earn: 3

Prerequisite(s): CHS 713

Grading Basis: Graded
Units of Lecture: 3
Offered: Every Spring - Even Years

Student Learning Outcomes
Upon completion of this course, students will be able to:
1. explain assumptions required for causal inference from public health observational studies.
2. select appropriate causal inference models for given public health datasets and scientific questions.
3. implement various causal inference statistical methods using a statistical software R.
4. interpret causal inference analysis results.

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