Description
This course explores how we can make policy recommendations using data. The overall goal of the course is to provide a survey of the most commonly-used empirical tools for political science and public policy research. Our focus is design-based causal inference, or the use of statistical methods to answer research questions that concern the impact of some cause (e.g., an intervention, a change in institutions, passage of a law, changes in economic conditions, or policies) on a certain outcome (e.g., vote choice, income, election results, levels of violence, political attitudes). We cover a variety of causal inference designs and methods, including experiments, regression, matching, difference-in-differences, and regression discontinuity designs. We will analyze the strengths and weaknesses of these methods using applications from the real world.
Slides
1 Introduction and Course Overview
2 The Potential Outcomes Framework
3 Omitted Variable Bias and Selection Into Treatment
4 Experiments and Randomization
6 Spillovers, Noncompliance, and Attrition
7 Interaction Effects and Heterogeneous Treatment Effects
15 Encouragement Experiments and Fuzzy RD
Assignments