Causal Data Science for Business Analytics

6 ECTS Module for Master Students

Nature Computational Science: Quantifying causality in data science with quasi-experiments


  • 6 ECTS module
  • 2 courses Causal Data Science (Lecture) & Business Analytics with Causal Data Science (Problem-based Learning)

Target Audience

  • Master students in “Data Science” and “Internationales Wirtschaftsingenieurwesen (IWI)”
  • Students with a strong interest and motivation in acquiring the skills required for mastering the causal aspects of modern business analytics.



Most managerial decision problems require answers to questions such as “what happens to Y if we do X?”, or “was it X that caused Y to change?” In other words, practical business decision-making requires knowledge about cause-and-effect. While most data science and machine learning approaches are designed to efficiently detect patterns in high-dimensional data, they are not able to distinguish causal relationships from simple correlations. That means, commonly used approaches to business analytics often fall short to provide decision makers with important causal knowledge. Therefore, many leading companies currently try to develop specific causal data science capabilities.

This module will provide an introduction into the topic of causal inference with the help of modern data science and machine learning approaches and with a focus on applications to practical business problems from various management areas. Based on an overarching framework for causal data science, the course will guide students to detect sources of confounding influence factors, understand the problem of selective measurement in data collection, and extrapolate causal knowledge across different business contexts. We also cover several tools for causal inference, such as A/B testing and experiments, difference-in-differences, instrumental variables, matching, regression discontinuity designs, etc. 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.


After completing this module, students will be able to:

  • Understand the difference between “correlation” and “causation”
  • Understand the shortcomings of current correlation-based approaches
  • Develop causal knowledge relevant for specific data-driven decisions
  • Discuss the conceptual ideas behind various causal data science tools and algorithms
  • Carry out state-of-the-art causal data analyses


  • Students are evaluated based on their solutions of challenges assigned in each session, which they continuously document in their lab journals.


  • Please register for the entire module Causal Data Science for Business Analytics here: E-Learning StudIP

Time & Location

  • Causal Data Science (Lecture): Monday, 11.30 - 13.00, Building D, Room D - 1.023
  • Business Analytics with Causal Data Science (PBL): Tuesday, 15.00 - 16.30, Building O, Room O - 0.007

Course Notes & Materials

Access to course notes & materials will be published on StudIP.

Preliminary Schedule

1April 15 & 16Introduction to Causal Inference
2April 22 & 21Graphical Causal Models
3April 29 & 30Randomized Experiments & Linear Regression
4May 6 & 7Matching
5May 13 & 14Double Machine Learning
-May 20 & 21Holiday
6May 27 & 28Effect Heterogeneity
7June 3 & 4Unobserved Confounding & Instrumental Variables
8June 10 & 11Difference-in-Difference
9June 17 & 18Synthetic Control
10June 24 & 25Regression Discontinuity
11July 1 & 2Causal Mediation
12July 8 & 9Further Topics in Causal Machine Learning



  • Ding, Peng (2023). A First Course in Causal Inference. arXiv preprint arXiv:2305.18793.
  • Facure, Matheus (2023). Causal Inference in Python - Applying Causal Inference in the Tech Industry. O’Reilly Media.
  • Huber, Martin (2023). Causal analysis: Impact evaluation and Causal Machine Learning with applications in R. MIT Press, 2023.
  • Neal, Brady (2020). Introduction to causal inference from a Machine Learning Perspective. Course Lecture Notes (draft).


  • Angrist, J. D., & Pischke, J. S. (2014). Mastering metrics: The path from cause to effect. Princeton university press.
  • Cunningham, Scott (2021). Causal Inference: The Mixtape, New Haven: Yale University Press.
  • Gertler, Paul J., et al. (2016). Impact evaluation in practice. World Bank Publications.
  • Hernán Miguel A., and Robins James M. (2020). Causal Inference: What If. Boca Raton: Chapman & Hall/CRC.
  • Huntington-Klein, Nick (2021). The effect: An introduction to research design and causality. Chapman and Hall/CRC.
  • Imbens, G. W., & Rubin, D. B. (2015). Causal inference in statistics, social, and biomedical sciences. Cambridge University Press.
  • Mullainathan, Sendhil, and Jann Spiess. (2017). Machine Learning: An Applied Econometric Approach. Journal of Economic Perspectives, 31(2): 87–106.
  • Pearl, Judea, and Dana Mackenzie (2018). The Book of Why. Basic Books, New York, NY.
  • Pearl, Judea, Madelyn Glymour, and Nicholas P. Jewell (2016). Causal Inference in Statistics: A Primer. John Wiley & Sons, Inc., New York, NY.
  • Peters, Jonas, Dominik Janzing, and Bernhard Schölkopf (2017). Elements of causal inference: foundations and learning algorithms. The MIT Press.
Prof. Dr. Christoph Ihl
Professor & Head of Institute

My research interests include cultural entrepreneurship, social networks and natural language processing.

Oliver Mork
Research Assistant & Doctoral Student

My research interests lie at the intersection of Econometrics & Machine Learning.