Welcome to POLI 171 at UCSD

by Yiqing Xu

This 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 using applications from the real world.

From a skill-builiding point of view, this course has three objecives:

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

2017 Course Materials

Lecture Slides

Policy Briefings

I disccus one real-world application at the beginning of each class.