Overview
Description
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.
Requirements
Prerequisites
CHS 713
Original catalog text
Prerequisites
Prerequisite(s): CHS 713
Units
Lecture3
Catalog Details
Offering
Offered: Every Spring - Even Years
Terms
spring
Attributes
Standard
Learning Outcomes
- explain assumptions required for causal inference from public health observational studies.
- select appropriate causal inference models for given public health datasets and scientific questions.
- implement various causal inference statistical methods using a statistical software R.
- interpret causal inference analysis results.