Teaching

• POLS 4700: Mathematics and Statistics for Political Science
  This course presents basic mathematical and statistical concepts that are essential for formal and quantitative analysis in political science research. It prepares students for the graduate-level sequence on formal models and quantitative political methodology offered in the department. The first half of the course will cover basic mathematics, such as calculus and linear algebra. The second half of the course will focus on probability theory and statistics. We will rigorously cover the topics that are directly relevant to formal and quantitative analysis in political science such that students can build both intuitions and technical skills. There is no prerequisite. The course is aimed for both students with little exposure to mathematics and those who have taken some courses but wish to gain a more solid foundation.

• POLS 4722: Statistical Theory and Causal Inference
  This course is the second course in the graduate-level sequence on quantitative political methodology offered in the Department of Political Science. Students will learn (1) a framework and methodologies for making causal inferences from experimental and observational data, and (2) statistical theories essential for causal inference. Topics include randomized experiments, estimation under ignorability, instrumental variables, regression discontinuity, difference-in- differences, and causal inference with panel data. We also cover statistical theories, such as theories of ordinary least squares and maximum likelihood estimation, by connecting them to causal inference methods. This course builds on the materials covered in POLS 4700 and 4720 or their equivalent (i.e., probability, statistics, linear regression, logistic regression, and knowledge of statistical computing environment R).

• POLS 4726: Topics in Political Methodology
  This course is the fourth course in the graduate-level sequence on quantitative political methodology offered in the Department of Political Science. Students will learn a variety of advanced topics in political methodology, such as machine learning, recent measurement methods (e.g., ideal point estimation, text analysis, list experiment, and conjoint experiment), network analysis, and causal inference with network and spatial data. Students will collaborate to present discussion papers throughout the semester. The main goal of this course is to help students to write a final paper that applies or develops advanced statistical methods. This course builds on the materials covered in POLS 4700, 4720, 4722, and 4724, or their equivalent courses (i.e., probability, statistics, linear regression, logistic regression, causal inference with observational and experimental data, and knowledge of statistical computing environment R).