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2023/2024  KAN-CCMVI2103U  Causal Data Science for Business Decision Making

English Title
Causal Data Science for Business Decision Making

Course information

Language English
Course ECTS 7.5 ECTS
Type Elective
Level Full Degree Master
Duration Summer
Start time of the course Summer
Timetable Course schedule will be posted at calendar.cbs.dk
Min. participants 30
Max. participants 60
Study board
Study Board for cand.merc. and GMA (CM)
Course coordinator
  • Paul Hünermund - Department of Strategy and Innovation (SI)
For academic questions related to the course, please contact course responsible Paul Hünermund (phu.si@cbs.dk).
Main academic disciplines
  • Managerial economics
  • Statistics and quantitative methods
  • Strategy
Teaching methods
  • Blended learning
Last updated on 07-03-2024

Relevant links

Learning objectives
At the end of the course, students should be able to:
  • Understand the crucial role of causal knowledge for data-augmented decision-making in strategic management
  • Have a precise understanding of what it means to say "correlation doesn’t imply causation"
  • Critically reflect on the shortcomings of current correlation-based approaches to machine learning and AI for business analytics
  • Discuss the conceptual ideas behind various causal data science tools and algorithms
  • Understand the importance of management theory for causal inference in business intelligence
  • Carry out state-of-the-art causal data analyses by themselves
Course prerequisites
Completed Bachelor degree or equivalent
Causal Data Science for Business Decision Making:
Exam ECTS 7,5
Examination form Written sit-in exam on CBS' computers
Individual or group exam Individual exam
Assignment type Written assignment
Duration 4 hours
Grading scale 7-point grading scale
Examiner(s) One internal examiner
Exam period Summer
Aids Limited aids, see the list below:
The student will have access to
  • Access to Canvas
  • basic IT application package
Make-up exam/re-exam
Same examination form as the ordinary exam
The number of registered candidates for the make-up examination/re-take examination may warrant that it most appropriately be held as an oral examination. The programme office will inform the students if the make-up examination/re-take examination instead is held as an oral examination including a second examiner or external examiner.
1st retake is the same as the ordinary exam.
Course content, structure and pedagogical approach

Most managerial decision problems require answers to questions such as “what happens if?”, “what is the effect of X on Y?”, or “was it X that caused Y to go up?” In other words, practical business decision-making requires knowledge about cause-and-effect. While standard tools in machine learning and AI are designed for efficient pattern detection in high-dimensional settings, they are not able to distinguish causal relationships from simple correlations in the data. That means, most commonly used approaches to machine learning fall short in addressing pressing questions in business analytics and strategic management. This creates an important mismatch between the answers that these algorithms can provide and the problems that managers and strategists would like to solve. Which is why, in recent years, several leading companies from the tech sector and elsewhere (among them: Amazon, Meta, Google, Uber, Spotify, Zalando and McKinsey) have started to heavily invest into their causal data science capabilities.


This course will provide an introduction into the topic of causal inference in machine learning and AI, with a focus on applications to practically relevant, data-driven business cases. The course is meant to be conceptual rather than technical, in order to bridge the gap between data science and management strategy, for better evidence-based decision-making. A variety of hands-on examples will be discussed that allow students to apply their newly obtained knowledge and carry out state-of-the-art causal analyses by themselves. The course will thereby loosely follow the structure of “The Book of Why” by Judea Pearl and Dana Mackenzie, which has ushered a new era of causal thinking in data science and machine learning upon its publication in 2018. In particular, students will be put into the position to detect sources of confounding influence factors that threaten valid causal conclusions, understand the problem of selective measurement in data collection, and extrapolate causal knowledge across different business contexts. By developing an overarching framework for causal data science, the course will also cover several standard tools for causal inference, which are often used in empirical research in business and economics (such as instrumental variables, regression discontinuity designs, A/B testing and experiments, etc.). Thus, while not a research methods course as such, this elective will nonetheless be highly relevant for students who plan to conduct a quantitative data analysis as part of their master thesis project.

Description of the teaching methods
The course consists of in-class lectures, guest lectures by practitioners from the tech sector, and hands-on tutorials in which students will learn how to carry out their own causal data analyses. In these practical sessions, state-of-the-art software for causal analysis will be used (www.causalfusion.net, no coding experience required). The course will incorporate (non-graded) problem sets, which can be done either individually or in groups, and which will prepare students for the exam. No specific prior knowledge is required. However, basic concepts in statistics (conditional means, variances, hypothesis testing, regression) will be useful and therefore repeated at the beginning of the course. In-class lectures will feature case studies and applications to demonstrate the practical relevance of the covered material.
Feedback during the teaching period
Feedback will be provided on exercises and problem sets during class.
Student workload
Lectures 30 hours
Exercises 18 hours
Preparation 118 hours
Exam 40 hours
Further Information

6-week 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.




Expected literature

Core reading:


  • Pearl, Judea, and Dana Mackenzie (2018). The Book of Why. Basic Books, New York, NY.


Additional literature:



More literature to be announced in the syllabus.

Last updated on 07-03-2024