HPM 883: Advanced Quantitative Methods

PhD course · Health Policy & Management · UNC Gillings School of Global Public Health

HPM 883 is the third course in the Health Policy & Management PhD quantitative methods sequence. It covers experimental design, double/debiased machine learning, heterogeneous treatment effects with causal forests, and policy learning — the methodological toolkit for moving research from “does it work?” to “for whom?” to “what should we do?”

Per-semester course sites below. Materials from prior semesters are password-protected — email the instructor for access.

Course sites by semester

Spring 2027 ACTIVE

Course in build. First class January 2027. Carries forward improvements from the Spring 2026 retrospective: locked problem-set data, redesigned PS-4 prompts, capstone-aware schedule.

Site opens when materials are ready (target: early January 2027).

Spring 2026 ARCHIVE

Completed May 2026. 70% causal ML, 30% experimental design. Four problem sets, capstone project, jigsaw peer-teaching format.

Open archive →

Spring 2025 ARCHIVE

First teaching of the course. Establishes the experimental-design + causal-inference foundation that the Spring 2026 redesign built on.

Open archive →

About

Instructor: Sean Sylvia — personal site · UNC Gillings profile. Department of Health Policy and Management, UNC Gillings School of Global Public Health.

Research group: DHEPLab (Digital Health Economics & Policy Lab) — methods research on AI evaluation, value-based care, and behavioral economics in health.

Course org on GitHub: github.com/unc-hpm-quant — per-semester repos for syllabus, slides, problem sets, and solutions.

Cross-campus interest: PhD students from biostatistics, statistics, business, and computer science have audited this course. Email the instructor to discuss enrollment.