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The goal of this course is to deepen students’ expertise in causal inference methods for quantitative health services research, especially methods students may have touched on only briefly in other courses. The class will particularly focus on natural experiments and matching methods that students can employ to evaluate the effects of social and biological exposures, healthcare access, and the public policies that govern them. Covered content will include econometrics (e.g., difference-in-differences and its extensions; instrumental variables; RDD; etc.) as well as more traditional epidemiologic approaches (i.e., matching methods and their extensions). Students will use statistical software to estimate models, interpret results, and gain a clear sense of how to apply these methods in practice.

College/Department: Dornsife School of Public Health
Repeat Status: Not repeatable for credit
Restrictions: Can enroll if classification is PhD.
Prerequisites: BST 560 [Min Grade: B-] and HMP 810 [Min Grade: B-]