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Course List

PSCI 504 Causal Inference

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  • Spring 2020
    Anderson Frey
    Spring 2020 — TR 9:40 - 10:55
    Course Syllabus

    The goal of this course is to give students a comprehensive toolbox for reading and producing cutting-edge applied empirical research, with focus on the theory and practice behind causal inference in social sciences. We will cover treatment effects, experiments, panel data, differences-in-differences, instrumental variables, nonparametric regression, regression discontinuity, matching, synthetic control, and more. Students will read applied papers from both political science and economics, and write review reports examining research designs, identification strategies, and causal claims. They will also produce research proposals that will be presented in class. Applications will be taught with R.

  • Spring 2019
    Anderson Frey
    Spring 2019 — TR 10:30 - 12:00
    Course Syllabus

    The goal of this course is to give students a comprehensive toolbox for reading and producing cutting-edge applied empirical research, with focus on the theory and practice behind causal inference in social sciences. We will cover treatment effects, experiments, panel data, differences-in-differences, instrumental variables, nonparametric regression, regression discontinuity, matching, synthetic control, and more. Students will read applied papers from both political science and economics, and write review reports examining research designs, identification strategies, and causal claims. They will also produce research proposals that will be presented in class. Applications will be taught with R.

  • Spring 2018
    Anderson Frey
    Spring 2018 — TR 14:00 - 15:15
    Course Syllabus

    The goal of this course is to give students a comprehensive toolbox for reading and producing cutting-edge applied empirical research, with focus on the theory and practice behind causal inference in social sciences. We will cover treatment effects, experiments, panel data, differences-in-differences, instrumental variables, nonparametric regression, regression discontinuity, matching, synthetic control, and more. Students will read applied papers from both political science and economics, and write review reports examining research designs, identification strategies, and causal claims. They will also produce research proposals that will be presented in class. Applications will be taught with R.

  • Fall 2016

    W 1400-1515 & F 1030-1200. The goal of this course is to give students a comprehensive toolbox for reading and producing cutting-edge applied empirical research, with focus on the theory and practice behind causal inference in social sciences. We will cover treatment effects, experiments, panel data, differences-in-differences, instrumental variables, nonparametric regression, regression discontinuity, matching, synthetic control, and more. Students will read applied papers from both political science and economics, and write review reports examining research designs, identification strategies, and causal claims. They will also produce research proposals that will be presented in class. Applications will be taught with R.

  • Spring 2013
    Matthew Blackwell
    Spring 2013 — R 15:30 - 18:10
    Course Syllabus

    Substantive questions in empirical social science research are often causal. Does voter outreach increase turnout? Do political institutions affect economic development? Are job training programs effective? This class will introduce students to both the theory and the practice behind making these kinds of causal inferences. We will cover causal identification, potential outcomes, experiments, matching, regression, difference-in-differences, instrumental variables estimation, regression discontinuity designs, sensitivity analysis, dynamic causal inference, and more. The course will draw upon examples from political science, economics, sociology, public health, and public policy.

  • Fall 2012
    Matthew Blackwell
    Fall 2012 — R 15:30 - 18:10

    Substantive questions in empirical social science research are often causal. Does voter outreach increase turnout? Do political institutions affect economic development? Are job training programs effective? This class will introduce students to both the theory and the practice behind making these kinds of causal inferences. We will cover causal identification, potential outcomes, experiments, matching, regression, difference-in-differences, instrumental variables estimation, regression discontinuity designs, sensitivity analysis, dynamic causal inference, and more. The course will draw upon examples from political science, economics, sociology, public health, and public policy.