Advisory Prerequisites: Biostats 601, 602, 650, and 651
Description: This course is designed to introduce students to basic of causal inference, including potential outcomes, counterfactuals, confounding, mediation, and instrumental variables. We will explore the identification and estimation of causal effects via the use of principle stratification, marginal structural models, and directed acyclic graphs.
Learning Objectives: At the end of the course, students should be able to
• Define concepts including potential outcomes, confounding, and mediation.
• Explain the purpose of randomization for causal inference.
• Explain the concept of principal stratification/casual association and use the relevant statistical methods to make causal inference under the casual association paradigm.
• Explain the concepts of direct and indirect effects/casual effects and use the relevant statistical methods to make causal inference under the casual effects paradigm.
• Understand how instrumental variables can be related to other causal inference approaches.
• Consider the use of directed acyclic graphs (DAGs) to define causal concepts and derive conditions of identifiability.