In most of economics, marketing, and business management, we are interested in causal relationships between variables, rather than mere correlations. For example, it is not the correlation between marketing expenses and sales that is of interest, but the effect of increasing marketing expenses for a product on the sale volume of the same product. Similarly, we are interested in understanding, for example, the causal effect of an environmental policy on pollution, human behavior, or business revenue rather than how these measures correlate. In this course, we study methods for estimating and identifying such causal effects.
First, the course provides a brief review of basic regression techniques. Second, we introduce the topic of causal analysis. We will define causal effects based on the potential outcomes framework, encounter the fundamental problem of causal analysis, and discuss what separates association from causation. In the third part of the course, we discuss designs and methods for data from observational studies including instrumental variables, difference-in-difference, event study design, and regression discontinuity design. Examples from the literature and step-by-step tutorials offer hands-on experience in utilizing the methods.
Preliminary course outline:
- Short review of basic regression techniques (inference, asymptotics and dummy variables).
- Causal inference using potential outcomes.
- Randomized experiments.
- Regression and causality.
- Instrumental variables.
- Fixed effects and panel data.
- Differences-in-differences and event study design.
- Regression discontinuity design.