This workshop series gives an overview of newly emerged causal inference methods using panel data (with dichotomous treatments). We start our discussion with a review of the difference-in-differences (DiD) method and conventional two-way fixed effects (2WFE) models. We then discuss the drawbacks of 2WFE models from a design-based perspective and clarify the two main identification regimes: one under the strict exogeneity (SE) assumption (or its variants) and one under the sequential ignorability (SI) assumption. In Lecture 2, we review the synthetic control method and discuss its extensions. In Lecture 3, we introduce the factor-augmented approach, including panel factor models, matrix completion methods, and Bayesian latent factor models. In Lecture 4, we take a different route and discuss matching and reweighting methods to achieve causal inference goals with panel data under the SE or SI assumptions. We also discuss hybrid methods that enjoy doubly robust properties.
Lecture 1. Difference-in-Differences and Fixed Effects Models
Lecture 2. Synthetic Control and Extensions
Lecture 3. Factor-Augmented Methods
Lecture 4. Matching/Balancing and Hybrid Methods
This is the first course in a four-course sequence on quantitative political methodology at Stanford Political Science. Political methodology is a growing subfield of political science which deals with the development and application of statistical methods to problems in political science and public policy. The subsequent courses in the sequence are 450B, 450C, and 450D. By the end of the sequence, students will be capable of understanding and confidently applying a variety of statistical methods and research designs that are essential for political science and public policy research.
This first course provides a graduate-level introduction to regression models, along with the basic principles of probability and statistics which are essential for understanding how regression works. Regression models are routinely used in political science, policy research, and other disciplines in social science. The principles learned in this course also provide a foundation for the general understanding of quantitative political methodology. If you ever want to collect quantitative data, analyze data, critically read an article that presents a data analysis, or think about the relationship between theory and the real world, then this course will be helpful for you.
You can only learn statistics by doing statistics. In recognition of this fact, the homework for this course will be extensive. In addition to the lectures and weekly homework assignments, there will be required and optional readings to enhance your understanding of the materials. You will find it helpful to read these not only once, but multiple times (before, during, and after the corresponding homework).
Overview. Data science is quickly changing the way we understand and engage in politics, how we implement policy, and how organizations across the world make decisions. In this course, we will learn the fundamental tools of data science and apply them to a wide range of political and policy-oriented questions. How do we predict presidential elections? How can we guess who wrote each of the Federalist Papers? Do countries become less democratic when leaders are assassinated? These are just a few of the questions we will work on in the course.
Learning Goals. The course has three basic learning goals for students. At the end of this course, students should:
Learning Approach. Statistical and programming concepts do not lend themselves to the traditional lecture format, and in general, experimental research on teaching methods shows that combining active learning with lectures outperforms traditional lecturing. We will teach each concept in lectures using applied examples that encourage active learning. Lectures will be broken up into small modules; first, I will explain a concept, and then we will write code to implement the concept in practice. Students are asked to bring their laptops to class so that we can actively code during lectures. This will help students “learn by doing” and it will ensure that the transition from lecture to problem sets is smooth.
This undergraduate-level course explores how we can make policy recommendations using data. The overall goal of this course is to introduce a basic framework for policy evaluation – what we call design-based causal inference – essentially, how we can use statistical methods to answer research questions that concern the impact of some cause on certain policy outcomes. We cover the mostly commonly used research designs, including randomized experiments, selection on observables, and difference-in-differences, and analyze the strengths and weaknesses of these methods. We discuss a real-world application at the beginning of each class.
From a skill-builiding point of view, this course has three objecives:
This is the third graduate-level course in the quantitative political methodology sequence at the Political Science Department at UCSD. The goal of course is to provide a survey of most commonly used empirical tools for political science and public policy research. Our focus is design-based causal inference, that is, to use statistical methods to answer research questions that concern the impact of some cause on certain outcomes. We cover a variety of causal inference designs and methods, including experiments, matching, regression, fixed effects models, difference-in-differences, synthetic control methods, instrumental variable estimation, and regression discontinuity designs.
The class is open to qualified students from other departments and undergraduates, but priority will be given to graduate students in the Political Science Department.
This course provides an overview of China’s recent history and its political system. We will begin with a historical overview of China’s political development since the late Qing. The remainder of the course will examine the institutional features of the Chinese political system and the key challenges facing the CCP leadership, such as economic reforms, regime stability, pollution, and political reform. We will also invite world-renowned experts in various areas of China studies to speak in our class.