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.

Prerequisites: Undergraduates must obtain the instructor's (or a Political Science advisor's) permission to take this course. Students must have taken a sequence in calculus and have attended the Political Science two-week Math Bootcamp. The Math Bootcamp may be waived in rare cases where a student has already taken courses in multivariable calculus, linear algebra, and probability.

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.

Prerequisites: Undergraduates must obtain the instructor's (or a Political Science advisor's) permission to take this course. Students must have taken a sequence in calculus and have attended the Political Science two-week Math Bootcamp. The Math Bootcamp may be waived in rare cases where a student has already taken courses in multivariable calculus, linear algebra, and probability.

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.

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.

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.

Prerequisites: Undergraduates must obtain the instructor's (or a Political Science advisor's) permission to take this course. Students must have taken a sequence in calculus and have attended the Political Science two-week Math Bootcamp. The Math Bootcamp may be waived in rare cases where a student has already taken courses in multivariable calculus, linear algebra, and probability.

In this course, we will examine the linear regression model and its variants. The course has two goals: (1) to provide students with the statistical theory of the linear model, and (2) to provide students with skills for analyzing data. The linear model is a natural starting point for understanding regression models in general, inferences based on them, and problems with our inferences due to data issues or to model misspecification. The model's relative tractability has made it an attractive tool for political scientists, resulting in volumes of research using the methods studied here. Familiarity with the linear model is now essentially required if one wants to be a consumer or producer of modern political science research.

In this course, we will examine the linear regression model and its variants. The course has two goals: (1) to provide students with the statistical theory of the linear model, and (2) to provide students with skills for analyzing data. The linear model is a natural starting point for understanding regression models in general, inferences based on them, and problems with our inferences due to data issues or to model misspecification. The model's relative tractability has made it an attractive tool for political scientists, resulting in volumes of research using the methods studied here. Familiarity with the linear model is now essentially required if one wants to be a consumer or producer of modern political science research.

In this course, we will examine the linear regression model and its variants. The course has two goals: (1) to provide students with the statistical theory of the linear model, and (2) to provide students with skills for analyzing data. The linear model is a natural starting point for understanding regression models in general, inferences based on them, and problems with our inferences due to data issues or to model misspecification. The model's relative tractability has made it an attractive tool for political scientists, resulting in volumes of research using the methods studied here. Familiarity with the linear model is now essentially required if one wants to be a consumer or producer of modern political science research.

In this course, we will examine the linear regression model and its variants. The course has two goals: (1) to provide students with the statistical theory of the linear model, and (2) to provide students with skills for analyzing data. The linear model is a natural starting point for understanding regression models in general, inferences based on them, and problems with our inferences due to data issues or to model misspecification. The model's relative tractability has made it an attractive tool for political scientists, resulting in volumes of research using the methods studied here. Familiarity with the linear model is now essentially required if one wants to be a consumer or producer of modern political science research.

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.

Prerequisites: Undergraduates must obtain the instructor's (or a Political Science advisor's) permission to take this course. Students must have taken a sequence in calculus and have attended the Political Science two-week Math Bootcamp. The Math Bootcamp may be waived in rare cases where a student has already taken courses in multivariable calculus, linear algebra, and probability.

In this course, we will examine the linear regression model and its variants. The course has two goals: (1) to provide students with the statistical theory of the linear model, and (2) to provide students with skills for analyzing data. The linear model is a natural starting point for understanding regression models in general, inferences based on them, and problems with our inferences due to data issues or to model misspecification. The model's relative tractability has made it an attractive tool for political scientists, resulting in volumes of research using the methods studied here. Familiarity with the linear model is now essentially required if one wants to be a consumer or producer of modern political science research.

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.

Prerequisites: Undergraduates must obtain the instructor's (or a Political Science advisor's) permission to take this course. Students must have taken a sequence in calculus and have attended the Political Science two-week Math Bootcamp. The Math Bootcamp may be waived in rare cases where a student has already taken courses in multivariable calculus, linear algebra, and probability.

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.

Prerequisites: Undergraduates must obtain the instructor's (or a Political Science advisor's) permission to take this course. Students must have taken a sequence in calculus and have attended the Political Science two-week Math Bootcamp. The Math Bootcamp may be waived in rare cases where a student has already taken courses in multivariable calculus, linear algebra, and probability.

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.

Prerequisites: Undergraduates must obtain the instructor's (or a Political Science advisor's) permission to take this course. Students must have taken a sequence in calculus and have attended the Political Science two-week Math Bootcamp. The Math Bootcamp may be waived in rare cases where a student has already taken courses in multivariable calculus, linear algebra, and probability.

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.

Prerequisites: Undergraduates must obtain the instructor's (or a Political Science advisor's) permission to take this course. Students must have taken a sequence in calculus and have attended the Political Science two-week Math Bootcamp. The Math Bootcamp may be waived in rare cases where a student has already taken courses in multivariable calculus, linear algebra, and probability.