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Causal Inference and Reasoning

Kurs ID
Art des Kurses
BSc Kurs
FS 2024
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].
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.
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
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.
Stock and Watson (2019): Introduction to Econometrics. Pearson. 4th edition.
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.
Written examination.
Familiarity with the topics covered in Mathematics I and II, as well as those in Statistics I and II is assumed.
180 hrs
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