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