teaching

In collaboration with my methodology colleagues in the department, I developed the graduate and undergraduate methods sequences at Stanford, emphasizing rigorous training in quantitative and empirical research tools. Before joining Stanford, I designed a similar sequence at MIT.

POLISCI 450A: Political Methodology I

An introduction to quantitative political methodology, this course covers the foundational principles of probability, statistics, and regression models essential for political science research. Students will develop skills in statistical reasoning and data analysis, focusing on applying linear regression to real-world problems.
Syllabus (PDF)

POLISCI 450B: Political Methodology II - Causal Inference

This course explores advanced tools for causal inference, focusing on understanding and applying experimental designs, matching, difference-in-differences, synthetic control methods, instrumental variables, and regression discontinuity designs. It prepares students to evaluate and conduct empirical research in political science and public policy.
Syllabus (PDF)

POLISCI 450C: Model-Based Inference and Machine Learning

This course bridges model-based theories of inference and cutting-edge machine learning techniques. Topics include generalized linear models, regularization, missing data, latent-variable models, and machine learning approaches like tree-based models and LASSO. Students learn to evaluate complex statistical models and their contributions to political science.
Syllabus (PDF)

POLISCI 450D: Advanced Topics in Political Methodology

This capstone course in the quantitative methodology sequence covers advanced empirical tools for political science research. Topics include conjoint experiments, causal mediation and moderation, randomization inference, and Bayesian inference. The course emphasizes the application of these methods to students’ own research projects.
Syllabus (PDF)

POLISCI 150C/355C: Causal Inference for Social Science

An undergraduate level introduction to modern causal inference methods. This course covers experimental designs, matching, regression, instrumental variables, and regression discontinuity methods with applications across public policy, sociology, and economics.
Syllabus (PDF)

POLISCI 481: Designing Your Dissertation, Part I

A graduate seminar guiding students through the development of their dissertation prospectus. The course emphasizes defining research questions, reviewing existing literature, developing theoretical frameworks, and designing research methodologies. Collaboration and feedback are central to the course, helping students refine their research at every stage.
Syllabus (PDF)

Short Courses

In addition to full-length courses, I regularly teach the following short courses:

• Conjoint Experiments: Learn the design and analysis of conjoint experiments, a method widely used in social sciences to understand multidimensional preferences.
• Explainable Machine Learning: Explore techniques for making machine learning models interpretable and explainable for policy and social science applications.
• Field Experiments: Gain insights into the design, implementation, and analysis of field experiments to rigorously evaluate interventions in real-world settings.