Causal Data Science for Business Analytics
Complementary elective course in “Business & Management”
- Master students with “Business & Management” courses in their curriculum
- Students with a strong interest and motivation in acquiring the skills required for mastering the computational 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.
Causal Data Science for Business Analytics:: E-Learning StudIP
Time & Location
|1||Monday, November 6th||Q - 1.121|
|2||Monday, November 27th||Q - 1.121|
|3||Monday, December 18th||Q - 1.121|
Course Notes & Materials
Access to course notes & materials will be published on StudIP.
- Probability Theory
- Statistical Concepts
- Regression and Statistical Inference
- Directed Acyclic Graphs
- Randomized Controlled Trials
- Matching and Subclassification
- Instrumental Variables
- Regression Discontinuity
- 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.
- 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.