Course Schedule and Catalog

Department: Statistics

Meeting: 4:00PM - 5:15PM TR (9-JAN-2023 - 21-APR-2023) 

Part of Term: Full Term

Grade Mode: Standard Letter

Course Type: Lecture

Language of Instruction: Taught in English

Method of Instruction: Face to Face

Credit Hours: 3

 

Section Max Enrollment: 30

Section Enrolled: 18

Enrollment data as of: 12-JAN-2025 3:59PM

 

 

Final Exam: GR Course-Dept Schedules Exam

 

Description: Correlation is not causation, but what exactly is causation? In this course we will explore the framework statistical science has formalized to approach causation. We will examine the potential outcomes concept, counterfactual reasoning and directed acyclic graph (DAG) models. The course will cover key theorems and results in the field as well as practical estimation and inferential techniques. The course will address instrumental variables as well as exploratory use of causal methods for model building and design of studies. We will survey applications through case studies in various disciplines. After taking this course students will be able to construct causal models, estimate causal effects and distinguish what data are relevant and irrelevant for causal analysis. Students will also be introduced to software techniques using R. Recommended Prerequisite(s): The course is open to all graduate students, but students should be aware that this is a graduate level course in statistics. Students should be performing graduate level research in their respective fields and/or have a background in statistical inference and research methods.