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 graduatelevel 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 graduatelevel
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, differencein 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 graduatelevel
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).