Causal Inference and Reasoning - Q3
Participation Prerequisites
Familiarity with the topics covered in Statistics I and II is assumed. In particular, it is highly recommended that participants revise the basic principles of probability theory and statistical inference, as well as estimation and inference in the bivariate linear regression model. The revision of these topics in the first session will be terse. A good summary is provided in Stock & Watson (2020, Chs. 2–5).
Course Content
The purpose of this course is to introduce state-of-the art econometric techniques for causal analysis and apply them to real world data sets. The methods covered in this course include an in-depth analysis of the workhorse in data science, viz. the multiple linear regression model and least-squares estimation. Subsequently, techniques for more complex data structures frequently encountered in applied work such as panel data and binary dependent variables are discussed. Finally, advanced estimation methods like instrumental variables and differences-in-differences are covered. The empirical analyses are implemented in RStudio, the most popular data science software environment, and in RMarkdown, the prime language for producing replicable research.
Intended Learning Outcomes and Competencies
By the end of this course, students will be familiar with modern econometric techniques and will be able to apply them to real world data sets using state-of-the-art software. Students will have acquired a sound theoretical mindset for causal inference. They will have developed programming skills for conducting replicable empirical work. This proficiency will prove indispensable for their Bachelor’s thesis, for a Master’s degree or for data science projects in industry.
Instruction Type
In-class.
Form of Examination
The final grade is solely based on an in-person written exam at the end of the course. The exam will be closed-book and closed-notes.
Literature
Stock, J. H. & M. W. Watson (2020). Introduction to Econometrics. 4th ed. Boston: Pearson. ISBN: 9781292264455. URL: https://media.pearsoncmg.com/intl/ge/2019/cws/ge_stock_econometrics_4/.
Next events
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| 1/7 | Lecture | Mo, 12.01.2026 | 11:30 Uhr | 15:15 Uhr | IP-C-001 Family Business Auditorium Hörsaal / Lecture Hall |
| 2/7 | Lecture | Fr, 16.01.2026 | 11:30 Uhr | 15:15 Uhr | IP-C-001 Family Business Auditorium Hörsaal / Lecture Hall |
| 3/7 | Lecture | Mo, 19.01.2026 | 11:30 Uhr | 15:15 Uhr | IP-C-001 Family Business Auditorium Hörsaal / Lecture Hall |
| 4/7 | Lecture | Mo, 26.01.2026 | 11:30 Uhr | 15:15 Uhr | IP-C-001 Family Business Auditorium Hörsaal / Lecture Hall |
| 5/7 | Lecture | Mo, 02.02.2026 | 11:30 Uhr | 15:15 Uhr | IP-C-001 Family Business Auditorium Hörsaal / Lecture Hall |
| 6/7 | Lecture | Mo, 09.02.2026 | 11:30 Uhr | 15:15 Uhr | IP-C-001 Family Business Auditorium Hörsaal / Lecture Hall |
| 7/7 | Lecture | Fr, 13.03.2026 | 15:30 Uhr | 16:30 Uhr | IP-C-001 Family Business Auditorium Hörsaal / Lecture Hall |
Lecturers
Indicative Student Workload
| Self-Study | 141 h |
| Contact Time | 36 h |
| Examination | 3 h |