I teach graduate-level courses in quantitative methods and health economics, with emphasis on causal inference and applied econometrics for health policy research.
PhD-level | Spring Semester
Third course in the quantitative methods sequence for PhD students in Health Policy and Management. Focuses on causal machine learning methods, including heterogeneous treatment effects, double/debiased machine learning, and policy learning approaches.
My teaching emphasizes the connection between rigorous methodological training and real-world policy applications. I believe students learn quantitative methods best when they can immediately apply techniques to substantive health policy questions they care about.
In my courses, I integrate cutting-edge methodological developments with hands-on coding exercises and applied projects. Students work with real datasets and develop skills in both R and Python, preparing them for careers in research and policy analysis.
I welcome inquiries from prospective PhD students interested in health economics, digital health, or global health policy. I am particularly interested in students with strong quantitative backgrounds who are excited about applying rigorous methods to improve healthcare delivery and health systems.
If you are interested in working with me, please send your CV, a brief statement of research interests, and any relevant writing samples to sean.sylvia@unc.edu.