POLI 130B. Politics in the People’s Republic of China

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.

The class is open to UCSD undergraudate students.


POLI 273. Causal Inference

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.

Course Website

POLI 171. Making Policy with Data

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:

  1. Introduce an analytical framework for policy evaluation and related quantitative methods
  2. Introduce the most basic (and some of the most important) statistical concepts
  3. Equip students with basic coding skills with R

Course Website

MIT 17.802 Quantitative Methods II

Recitation 1: Potential Outcomes Model (PDF)

Recitation 3: Regression Recap and Experiment (PDF)

Recitation 5: Inference and Power Calculations (PDF)

Recitation 6: Selection on Observables and Match-Makers (PDF)

Lecture: Synthetic Control Methods (PDF)

Recitation 8: Instrumental Variables (PDF)

(I thank Josh Angrist for allowing me to use his course material in recitations.)

Evaluations: 6.6/7.0 (Excerpts of Anonymous Comments)

MIT 17.800 Quantitative Methods I

Syllabus (PDF)

R Basics in One Page (PDF)

Handout: Mid-term Review (PDF)

Handout: OLS Estimators (PDF); for more precise explanations of OLS asymptotics, here's a handout by Colin Cameron

Handout: Regression Diagnostics (PDF, Data & Script)

Evaluations: 5.8/7.0 (Excerpts of Anonymous Comments)

MIT Math Camp

Syllabus (PDF)

Slides 1 - Basics: Notations, Functions (PDF)

Slides 2 - Linear Algebra; OLS Geometrics (PDF)

Slides 3 - Calculus; OLS Asymptotics (PDF)

Slides 4 - R Session (PDF, Data Files): Simple Plots; Basic Parallel Computing; Accessing Remote Servers

(Slides borrow from Dustin Tingley and In Song Kim's math camp notes.)

Evaluations: 6.5/7.0 (Excerpts of Anonymous Comments)


From my awesome students:

Positive Political Economy

My Evolving Understanding of the Field: A Cognitive Map (PDF)