Picture of Naoki Egami
Naoki Egami

Curriculum Vitae

Email: naoki.egami at Columbia.Edu

Assistant Professor

Department of Political Science
Columbia University






  • POLS 4700: Mathematics and Statistics for Political Science
    (Fall 2020, Fall 2021)
    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
    (Spring 2021)
    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).