Caus­al In­fer­ence and Ap­plied Eco­no­met­rics for Busi­ness and So­cial Sci­ences

Course content

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.