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Module Number:
| 13715
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| Module Title: | Causal Data Science in Business and Economics |
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Kausale Datenanalyse in den Wirtschaftswissenschaften
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| Department: |
Faculty 5 - Business, Law and Social Sciences
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| Responsible Staff Member: | |
| Language of Teaching / Examination: | English |
| Duration: | 1 semester |
| Frequency of Offer: |
Every summer semester
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| Credits: |
6
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| Learning Outcome: | Students have a basic understanding of data science in the context of the identification of causal relationships. They are familiar with a verbal, and graphical language to communicate about causality, and with key concepts, such as counterfactuals, outcome equivalence, and confounding effects. They know about typical classes of problems that do not allow causal interpretations of observed associations as well as typical solutions for these problems by means of data analytic and data collection methods. Moreover, students understand the tight interdependency of data analytics and the design of data collection in order to generate high quality evidence and high quality predictions. |
| Contents: | - Counterfactuals, Potential Outcomes, Causal Graphs, and typical problems (i.e., omitted variables, measurement error, reverse causality, endogenous selection, endogenous treatment)
- Data analytic solutions: control variables, matching, weighting
- Data analytic solutions: instrumental variables, selection instruments
- Data collection solutions: real experiments
- Assumed experiments as mixed solutions: natural experiments, quasi experiments, regression discontinuity
- Times series data as mixed solution: diff-in-diff and related methods
- Reflections on moderation and mediation analyses respectively structural equation modeling
The module focuses on applications in business and economics, but the underlying theories and methods generalize beyond these fields. The course complements more traditional data science modules that have a stronger focus on the implementation of data scientific algorithms. Tutorials also apply these methods to the analysis of real-world problems and the analysis of simulated and real datasets. |
| Recommended Prerequisites: | None |
| Mandatory Prerequisites: | None |
| Forms of Teaching and Proportion: | -
Lecture
/ 2 Hours per Week per Semester
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Tutorial
/ 2 Hours per Week per Semester
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Self organised studies
/ 120 Hours
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| Teaching Materials and Literature: | - Pearl, J. (2009). Causality. Cambridge University Press, has become a classic reference in computer science.
- A more accessible introduction an be found in Morgan, S. L., & Winship, C. (2015). Counterfactuals and causal inference. Methods and Principles for Social Research. Cambridge University Press.
- A rather accessible econometric perspective on some aspects of the module is offered by Angrist, J. D., & Pischke, J. S. (2014). Mastering'metrics: The path from cause to effect. Princeton university press.
- More details on experiments can be found in Gerber, A. S., & Green, D. P. (2012). Field experiments: Design, analysis, and interpretation. WW Norton.
A few additional shorter articles or chapters will be provided during the course of the module. |
| Module Examination: | Final Module Examination (MAP) |
| Assessment Mode for Module Examination: | - written examination, 90 min
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| Evaluation of Module Examination: | Performance Verification – graded |
| Limited Number of Participants: | None |
| Part of the Study Programme: | -
Master (research-oriented) /
Angewandte Mathematik /
PO 2019
-
Master (research-oriented) /
Artificial Intelligence /
PO 2022
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Master (research-oriented) /
Betriebswirtschaftslehre /
PO 2017
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Master (research-oriented) /
Energietechnik und Energiewirtschaft /
PO 2021
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Master (research-oriented) /
Informatik /
PO 2008
- 2. SÄ 2017
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Bachelor (research-oriented) /
Mathematik /
PO 2019
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Master (research-oriented) /
Wirtschaftsingenieurwesen /
PO 2019
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Bachelor (research-oriented) /
Wirtschaftsmathematik /
PO 2019
- SÄ 2021
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| Remarks: | German-speaking (teaching and) examination staff is available if required.
Doctoral students are welcome to join the module; they do not need to participate in the exam; if they hand in a short term paper of sufficient quality, which discusses causality in one of their own studies or a study related to their own research, their successful participation will be certified. |
| Module Components: | - 530906 Lecture Causal Data Science in Business and Economics Aktuelle Beschreibung - 2 Hours per Week per Semester
- 530907 Exercise Causal Data Science in Business and Economics Aktuelle Beschreibung - 2 Hours per Week per Semester
- 530908 Examination Causal Data Science in Business and Economics Aktuelle Beschreibung
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| Components to be offered in the Current Semester: | |
