Causal Data Science for Business Analytics

6 ECTS Module for Master Students

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

Credits

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.

Instructor(s)

Prof. Dr. Christoph Ihl

Overview

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.

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
Formalize intuition about causal relationships using a “language” of causality
Derive causal hypotheses that can be tested with data
Discuss the conceptual ideas behind state-of-the-art causal data science tools and algorithms
Carry out causal data analyses with state-of-the-art tools

Grading

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

Registration

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

Session	Date	Topic
1	April 7 & 8	Introduction to Causal Inference
2	April 14 & 15	Graphical Causal Models
-	April 21 & 22	Holiday
3	April 28 & 29	Randomized Experiments & Linear Regression
4	May 5 & 6	Observed Confounding
5	May 12 & 13	Double Machine Learning
6	May 19 & 20	Effect Heterogeneity
-	May 26 & 27	Holiday
7	June 2 & 3	Unobserved Confounding & Instrumental Variables
-	June 9 & 10	Holiday
8	June 16 & 17	Difference-in-Differences
9	June 23 & 24	Synthetic Controls
10	June 30 & July 1	Regression Discontinuity
11	July 7 & 8	Causal Mediation
12	July 14 & 15	Causal Discovery

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.