Harvard University

Matching

(Gov 2002, Graduate, Fall 2015)

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 introduces 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 draws upon examples from political science, economics, sociology, public health, and public policy. Taught with R.

Class Page with Lecture Notes

All models are wrong, some are useful

(Gov 2000, Graduate, Fall 2016)

How can we detect voting irregularities? What causes individuals to vote? In what sense (if any) does democracy (or trade) reduce the probability of war? Quantitative political scientists address these questions and many others by using and developing statistical methods that are informed by theories in political science and the social sciences more generally. In this course, we provide an introduction to the tools used in basic quantitative social science research. The first four weeks of the course cover introductory univariate statistics, while the remainder of the course focuses on linear regression models. Furthermore, the principles learned in this course provide a foundation for the future study of more advanced topics in quantitative political methodology.

Class Page with Lecture Notes

Teaching methods in action

(Gov 2002, Graduate, Fall 2014 with Arthur Spirling)

A topics class in statistical methods. Topics covered includes Bayesian methods, causal inference, text analysis, item-response models, and others. Taught with a mix of lecture, discussion, and student presentations. Designed for second-year graduate students.


  • Causal Inference 1: Potential Outcomes and Selection on Observables (Slides)
  • Causal Inference 2: Instrumental Variables (Slides)
  • Causal Inference 3: Regression Discontinuity Designs (Slides)
  • Causal Inference 4: Panel Data (Slides)

University of Rochester

Matching?

(PSC 504, Spring 2013)

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. Taught with R, with a mix of lectures, discussion, and in-class computing.

Class Page

Galton correlation

(PSC 200, Fall 2012)

Data analysis has become a key part of many fields including politics, business, law, and public policy. This course covers the fundamentals of data analysis, giving students the necessary statistical skills to understand and critically analyze contemporary political, legal, and policy puzzles. Lectures focus on the theory and practice of quantitative analysis and weekly lab sessions guide students through the particulars of statistical software. No prior knowledge of statistics or data analysis is required. Taught with R.