Teaching

Teaching Fellow

Mental Health Care Policy in the US, Yale University (Spring 2025)

Instructor: Prof. Susan Busch

Class size: 16 students

Duties: Co-designed the midterm project; supported course instruction; provided feedback on student assignments.

Course outline: This course is designed for students with limited previous exposure to mental health policy. The goals of the course are to provide students with the tools to evaluate alternative mental health policies and to better understand potential effects, sometimes unintended, of government and private policies related to mental health care. By the end of the course students should be able to approach a problem in mental health policy by defining the policy, assessing possible solutions, and making a recommendation.

Health Politics, Governance, and Policy, Yale University (Fall 2023)

Instructor: Prof. Mark Schlesinger

Class size: 53 students

Duties: Delivered a guest session on youth mental health; ran monthly review sessions; advised students on final group projects.

Course outline: This course is designed to familiarize students with the various processes by which governmental health policy is made in the United States, and with current policy debates. One focus of the course is to understand the politics underlying the successes and failures of health policy making during the course of the twentieth century. This includes a discussion of the relevant governmental institutions, political actors, the major national programs that have been established, and how political actors use resources and set their strategies.

Methods in Health Services Research, Yale University (Spring 2023)

Instructor: Prof. Jacob Wallace

Class size: 122 students

Duties: Led weekly discussion sessions and R-based study halls; graded midterm and final exams.

Course outline: This course introduces students to quantitative social science methods—with an emphasis on causal inference—for health services research. The statistical concepts and methods are illustrated using data and examples primarily from the fields of health services research and health economics. The course begins with a refresher on linear regression (with a focus on intuition), progresses to causality and experimental research design, and then finishes with a tour of quasi-experimental techniques common in health services research: matching, difference-in-differences, regression discontinuity, instrumental variables, and synthetic control methods. Students learn to apply these techniques to data in the programming language of their choice (e.g., Stata/R) as well as gain general experience analyzing and visualizing data.