Causal Inference and Reasoning
Kurs ID
QUANT311
Art des Kurses
BSc Kurs
Wochenstunden
2,0
ECTS
3.0
Semester
FS 2024
Vortragssprache
Englisch
Vortragende/r
Prof. Dr. Michael Massmann
Bitte beachten Sie, dass AustauschstudentInnen im BSc-Programm der WHU eine höhere Anzahl an Credits erwerben als hier aufgeführt. Für weitere Informationen wenden Sie sich bitte direkt an das [International Relations Office].
Kursinhalt
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 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.
Termine
| Date | Time |
|---|---|
| Monday, 08.01.2024 | 11:30 - 15:15 |
| Monday, 15.01.2024 | 11:30 - 15:15 |
| Friday, 19.01.2024 | 17:15 - 18:45 |
| Monday, 22.01.2024 | 11:30 - 15:15 |
| Friday, 26.01.2024 | 10:00 - 11:30 |
| Monday, 29.01.2024 | 11:30 - 15:15 |
| Friday, 02.02.2024 | 09:30 - 11:00 |
| Monday, 05.02.2024 | 11:30 - 15:15 |
| Wednesday, 07.02.2024 | 19:00 - 20:30 |
| Tuesday, 13.02.2024 | 09:00 - 11:00 |
| Friday, 16.02.2024 | 11:30 - 15:15 |
| Wednesday, 28.02.2024 | 09:00 - 10:30 |
Lernerfolge
By the end of the course, students will be familiar with modern econometric and machine learning 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 and time series prediction. 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.
Literatur
Stock and Watson (2019): Introduction to Econometrics. Pearson. 4th edition.
Lernmethoden
The course takes the form of interactive lectures with exercises: on the one hand, theoretical material is discussed in class and illustrated by means of empirical examples in live demonstrations in RStudio; on the other hand, participants are given theoretical and coding exercises to practice the use of newly-learnt concepts.
Art der Prüfung
Written examination.
Voraussetzungen
Familiarity with the topics covered in Mathematics I and II, as well as those in Statistics I and II is assumed.
Umfang
180 hrs