

STAT 186/GOV 2002:
Causal Inference
Substantive questions in empirical scientific and policy research are often causal. Does voter outreach increase turnout? Are job training programs effective? Can a universal health insurance program improve people's health? This class will introduce students to both statistical theory and practice of causal inference. As theoretical frameworks, we will discuss potential outcomes, causal graphs, randomization and modelbased inference, sensitivity analysis, and partial identification. We will also cover various methodological tools including randomized experiments, regression discontinuity designs, matching, regression, instrumental variables, differenceindifferences, and dynamic causal models. The course will draw upon examples from political science, economics, education, public health, and other disciplines. Download the syllabus and the course materials. 
Gov 2003:
Topics in Quantitative Methodology
This course introduces social science students to applied Bayesian statistics. We will begin by introducing Bayes' rule, which allows us to learn from data in an intuitive and coherent way. We then cover a set of simple probabilistic models and cover powerful computational tools that will be used for the remainder of the course. Finally, we will learn about social science applications of Bayesian models including regression models, topic models, social network models, and dynamic models. Download the syllabus. 
POL 245:
Visualizing Data
In this course, we consider ways to illustrate compelling stories hidden in a blizzard of data. Equal parts art, programming, and statistical reasoning, data visualization is a critical tool for anyone who seeks to analyze data. In recent years, data analysis skills have become essential for those pursuing careers in policy advocacy and evaluation, business consulting and management, or academic research in the fields of education, health, medicine, and social sciences. This course introduces students to the powerful R programming language and the basics of creating dataanalysis graphics in R. From there, we use real datasets to explore topics ranging from network data (like social interactions on Facebook or trade between counties) to geographical data (like countylevel election returns in the US or the spatial distribution of insurgent attacks in Afghanistan). No prior background in statistics or programming is required or expected. Download the syllabus. 
POL 345:
Quantitative Analysis and Politics
What accounts for who votes and their choice of candidate? Would universal health insurance improve the health of the poor? Researchers and policy makers use statistics to answer these questions. However, the validity of their conclusions depends upon underlying assumptions and correct application of statistical methods. The course will introduce 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 independent research at Princeton and to become a critical consumer of news articles and academic studies that use statistics. Download the syllabus and the course materials. 
POL 451:
Statistical Methods in Political Science
In this course, students will learn basic research design and data analysis methodology in empirical social science research. The main goal is to learn how statistical theory can be used to make causal inferences in experimental and observational studies. The course satisfies the analytical methods requirement for politics majors. The materials of this course are particularly useful for those who plan to use quantitative analysis in their junior papers and senior thesis as well as for those who wish to apply for graduate programs in the social sciences. Familiarity with elementary probability theory is helpful, but is not required. Download the syllabus. 
POL 502:
Mathematics for Political Science
This course presents basic mathematical concepts that are essential for formal and quantitative analysis in political science research. It prepares students for advanced graduate courses offered in the department (e.g., POL 571573, 575576). The topics include real analysis, linear algebra, and probability theory. There is no prerequisite. The course is aimed for both students with little prior exposure to mathematics and those who have taken some courses in the past but wish to gain a more solid foundation. Undergraduate students who want to do the graduatelevel coursework in quantitative methods can also take the course for credit. Download the syllabus and handouts. 
Pol 571:
Quantitative Analysis I
This course is the first course in applied statistical methods for social scientists. Students will learn how statistical methods can be used to conduct causal inferences, exploratory data analysis, forecasting, and hypothesis testing. The first half of the course will be devoted to probability theory, which serves as a foundation of statistical theory. The second half covers the linear model in some depth and if time permits also introduces generalized linear models. An emphasis of the course is given to practical data analysis, and students will learn statistical programming as well as basic principles of probability theory and statistical inference. This course assumes the mathematical knowledge taught in POL 502, and prepares students for the next course in the sequence, POL 572. Download the syllabus and handouts. 
POL 572:
Quantitative Analysis II
This course is the first course in applied statistical methods for social scientists. Students will learn a variety of basic crosssection regression models (as time permits!) including linear regression model, discrete choice models, duration (or hazard) models, event count models, structural equation models, and others. Unlike traditional courses on applied regression modeling, I will emphasize the connections between these methods and causal inference, which is the primary goal of social science research. Prerequisites, POL 502 and POL 571. Download the syllabus and handouts. 
Pol 573:
Quantitative Analysis III
This course is the second course in applied statistical methods for social scientists. Building on the materials we covered in POL 572 or its equivalent (i.e., linear regression, structural equation modeling, instrumental variables, maximum likelihood estimation, discrete choice models), students will learn a variety of statistical methods including models for longitudinal data and survival data. Unlike traditional courses on applied regression modeling, I will emphasize the connections between these methods and causal inference, which is the primary goal of social science research. Download the syllabus and handouts. 
Pol 574:
Quantitative Analysis IV
The main goal of this course is to help students to write a publishable paper that uses advanced statistical methods. At the beginning of the semester, I will give brief introductory lectures on causal inference and applied Bayesian statistics to cover the fundamentals. Thereafter the materials covered will focus on the statistical methods appropriate for the projects selected by students. Download the syllabus. 
POL 722:
Probability and Statistics
This course presents basic principles of mathematical probability and statistics that are essential for advanced quantitative analysis in political science research. The first half of the course will cover basic probability theory, which serves as a foundation of statistical theory. The second half will be devoted to topics for statistical inference, which include estimation, hypothesis testing, asymptotic analysis and regression. Students are expected to complete twelve problem sets, one for each topic, each of which consists of four or five exercises. Download the syllabus 