BIOST 2068 - INTRODUCTION TO CASUAL INFERENCE

Minimum Credits: 3
Maximum Credits: 3
With the increasing demand for identifying causal effects, causal inference methods have been greatly developed in the last decades. In public health, most studies require more or less causal inference due to the prevalence of confounding and selection bias. This course will introduce 1) the concepts of causal effects, causal assumptions, and causal graphs, 2) widely-used causal inference methods (e.g., propensity score, instrumental variables, and causal mediation analysis), and 3) methods implementation and applications in public health. Students enrolling in this course are expected to have taken an introductory biostatistical course (BIOST 2038, 2039, or 2049) and familiar with programming. The course will be taught through lectures, followed by homework and a final project.
Academic Career: Graduate
Course Component: Lecture
Grade Component: Grad Letter Grade

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