The University of Texas at Austin
Gov 391K: Machine Learning in Political Science, Fall 2022 (Main Instructor)
Level: Graduate
Description: In this course, we will explore the universe of machine learning (ML) modeling strategies—their motivation, properties, and manner of implementation. Models examined will include tree-based methods, convolutional network networks, machine learning approaches for social networks, time series network models, ensembles, and probabilistic machine learning. We will interrogate how the use of these strategies can be used to support social science inquiry. We will also examine topics explicitly at the intersection of social science and ML itself—topics related to fairness, privacy, and the implications of ML for politics and society. By the end of the course, students should be able to employ state-of-art machine learning methods in their research and policy applications, understand the strengths and limitations of these methods, and know how to modify the proposed methods to maintain legal compliance, privacy, and ethics.
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Gov 391J: Statistical Analysis in Political Science I, Fall 2022 (Main Instructor)
Level: Graduate
Description: Social scientists seek to understand the social world, and this understanding is often critically based on the quantitative analysis of data. In this course, we introduce students to this mode of inquiry. We will explore classical approaches for bringing evidence to bear for empirical claims, examining their strengths, limitations, theoretical properties, and manner of implementation. Modeling strategies introduced will include probability models, linear regression, and some introductory causal inference approaches. Emphasis will also be placed on the effective communication of quantitative evidence. Therefore, we will emphasize throughout the course the importance of visualization methods, placebo tests, model robustness checks, and the qualitative meaning of the quantitative assumptions employed by various modeling strategies. By the end of the course, students should be able to tackle challenging empirical problems in their research with a variety of modeling strategies and to effectively communicate the implications of their analyses to their target audience.
Canvas link for enrolled students
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University of Tokyo
Introduction to Quantitative Social Science, Summer 2021 (Teaching Assistant)
Instructors: Kosuke Imai
Level: Undergraduate
Description: The course introduces basic principles of statistical inference and programming skills for data analysis. The goal is to provide students with the foundation necessary to analyze data in their own research and to become critical consumers of statistical claims made in the news media, in policy reports, and in academic research.
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Harvard University
Gov 2003 – Topics in Quantitative Methods, Spring 2019 (Teaching Fellow)
*Recipient, Certificate of Distinction in Teaching Award*
Instructors: Matthew Blackwell and Kosuke Imai
Level: Graduate
Description: This course covers topics of general interest to political methodology: causal inference, graphical models, mixed methods, contest modeling, text-as-data, item response. Illustrates how ideas and methods from these areas can be applied to substantive questions.
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Gov 2000 – Introduction to Quantitative Methods I, Fall 2018 (Teaching Fellow)
*Recipient, Certificate of Distinction in Teaching Award*
Instructor: Xiang Zhou
Level: Graduate
Description: An introduction to statistical research in political science with a focus on applied linear regression.
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Gov 62 – Research Practice in Qualitative Methods, Spring 2018 (Teaching Fellow)
*Recipient, Certificate of Distinction in Teaching Award*
Instructor: Frances Hagopian
Level: Undergraduate
Description: The primary objective of this seminar is to introduce students to the basic principles and tools of qualitative research in the social sciences. A second objective of the course is to prepare students to undertake original research for their senior thesis projects. The course therefore focuses on issues of qualitative research design and methodological application in comparative social science.
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Gov 2002 – Topics in Advanced Quantitative Methodology, Fall 2016 (Teaching Fellow)
Instructor: Michael Peress
Level: Graduate
Description: Graduate course on the theory and implementation of causal inference methods for social science research. Topics include randomized experiments, matching, diff-in-diff, instrumental variables, regression discontinuity designs, sensitivity analysis, and causal mediation.
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Math Prefresher: A Short Course in Quantitative Methods (Course Link)
(Summer 2016, Summer 2017; Main Instructor)
Level: Graduate
Description: A two-week graduate course on mathematics and computer programming for social scientists. The course introduces the mathematics and computer skills needed for quantitative and formal modeling courses offered at Harvard.
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