Overview

Distinguishing causal relationships from simple correlation is what commonly used approaches in business analytics often fall short of. In this course, we will provide you with the skill set to answer questions like

  • what happens to \(Y\) if we do \(X\)?
  • was it \(X\) that caused \(Y\) to change?

Introducing you to causal inference with the help of data science will allow you to carry out state-of-the-art causal analyses by yourself and extrapolate causal knowledge across different business contexts and various management areas.

Objectives

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

Instructors

  Lecture: Christoph Ihl

  Tutorial: Oliver Mork

Details

  Lecture: Monday, 11.30 - 13.00

  Tutorial: Tuesday, 15.00 - 16.30 + 16.45 - 18.15

Literature

Primary

  • 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).

Secondary

  • 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.