Causal Inference and Applied Econometrics for Business and Social Sciences

About

Understanding the difference between correlation and causation is critical for making data-driven decisions in both business and the social sciences. This course provides a rigorous introduction to causal inference and applied econometrics, with a focus on intuition and application. The course is ideal for professionals and students in business, economics, sociology, political science, and related fields who seek to apply econometric methods to real-world challenges.

Through a combination of theory and hands-on exercises, students will learn to apply causal inference techniques and models to real-world problems. More specifically, students will gain hands-on experience with the following techniques and concepts:

Directed Acyclic Graphs (DAGs)
Potential Outcome Framework
Matching Techniques
Regression Discontinuity Design (RDD)
Instrumental Variables (IV)
Panel Data and Difference-in-Differences (DiD)
Synthetic Control (Time-permitting)

Students will also develop practical skills in the use of statistical programming languages, such as Stata, R, or Python, to apply these methods directly to data. A working knowledge of one of these languages will be developed throughout the course as students apply the concepts and techniques to real-world data analysis tasks.

Course leader

Moira Daly

Target group

This is a bachelor level course. CBS Summer University courses at Copenhagen Business School is open to all and welcomes domestic and international students as well as professionals.

Fee info