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2024/2025  BA-BHAAI1112U  Causal Inference and Applied Econometrics for Business and Social Sciences

English Title
Causal Inference and Applied Econometrics for Business and Social Sciences

Course information
Language English
Course ECTS 7.5 ECTS
Type Elective
Level Bachelor
Duration Summer
Start time of the course Summer
Timetable Course schedule will be posted at calendar.cbs.dk
Min. participants 30
Max. participants 100
Study board
Study Board for BSc in Economics and Business Administration
Course coordinator
  • Moira Daly - Department of Economics (ECON)
Main academic disciplines
  • Statistics and quantitative methods
  • Economics
Teaching methods
  • Face-to-face teaching
Last updated on 07/11/2024

Relevant links
Learning objectives
Upon successful completion of the course, students should be able to:
  • Master advanced econometric tools to analyze causal relationships in business and social science data.
  • Demonstrate a strong understanding of key concepts such as randomization, unconfoundedness, directed acyclic graphs, potential outcome framework, matching techniques, regression discontinuity design, instrumental variables, panel data, differences-in-differences, and, time permitting, synthetic control.
  • Solidly grasp the intuition behind these techniques, recognize when each method is appropriate or not, and confidently apply this knowledge to actual data
  • Develop a working proficiency in a statistical programming language (Stata, R, or Python) to apply the concepts and techniques covered in the course.
  • Effectively communicate complex concepts and the results of quantitative analysis in a clear and concise manner, both in writing and visually, to diverse audiences, including those without a technical background.
Course prerequisites
Statistics
Examination
Causal Inference and Applied Econometrics for Business and Social Sciences:
Exam ECTS 7.5
Examination form Home assignment - written product
Individual or group exam Individual exam
Size of written product Max. 10 pages
Assignment type Written assignment
Release of assignment An assigned subject is released in class
Duration Written product to be submitted on specified date and time.
Grading scale 7-point grading scale
Examiner(s) One internal examiner
Exam period Summer and Summer
Make-up exam/re-exam
Same examination form as the ordinary exam
Same examination form as the ordinary exam
The 1st retake is a 72-hour, maximum 10-pages home assignment.
If the number of registered candidates for the make-up examination/re-take examination warrants that it may most appropriately be held as an oral examination, the programme office will inform the students that the make-up examination/re-take examination will be held as an oral examination instead.
Course content, structure and pedagogical approach

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.
Description of the teaching methods
Lectures and exercises. The class meetings are interactive and require ongoing engagement of the students.
Feedback during the teaching period
Non-graded exercises will be assigned and reviewed throughout the course.
Student workload
Preliminary assignment 20 hours
Classroom attendance 38 hours
Preparation 121 hours
Feedback activity 7 hours
Examination 20 hours
Further Information

6 weeks course. 

 

Preliminary Assignment: 

The course coordinator uploads Preliminary Assignment on Canvas at the end of May. It is expected that students participate as it will be included in the final exam, but the assignment is without independent assessment and grading.

 

We will discuss and review it during the first lecture.
Expected literature

Indicative course textbook: 


Cunningham, Scott. Causal Inference: The Mixtape. Yale University Press, 2021. https:/​/​doi.org/​10.2307/​j.ctv1c29t27.

Note that is a free online textbook that is updated, see the most recent version here: https:/​/​mixtape.scunning.com/​  , for a fee the book can be purchased in hard copy.

 
Last updated on 07/11/2024