Overview

Description

CALENDAR DESCRIPTION:

Provides an introduction to statistical methods used to analyze causal questions and evaluate policies. Discusses various approaches to drawing causal inferences from observational data.

COURSE DETAILS:

Economists and other social scientists often seek to measure the real-world effects of policy. More generally, we often want to assess the real-world effects of some potential “cause” on an “outcome.” For example, does a university degree increase future earnings? Does public health insurance make people healthier? Do environmental regulations reduce pollution? Do stricter capital requirements change bank lending behaviour?

This course will introduce you to the statistical and econometric methods that applied researchers use to answer causal questions like these. We will develop ideas in the potential outcomes framework and apply them to data using the R software package. Topics may include randomized experiments, regression discontinuity, matching, difference-in-differences, and instrumental variables. There will be regular graded assignments that will give you hands-on experience with data analysis in R. By the end of the course, you will learn how to critically evaluate statements about causal relationships, and apply a variety of methods to draw causal inferences of your own using R.

Topics:

1. Review of Statistical Methods
2. The Potential Outcomes Framework
3. Randomized Experiments
4. Introduction to Regression
5. Instrumental Variables
6. Regression Discontinuity Designs
7. Difference-in-Differences
8. Fixed Effects and Standard Errors

Grading

Materials

REQUIRED READING:

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REQUIRED READING NOTES: