123241-44 | B Advanced Methods | Causal Impact Evaluation

Lehrende: Prof. Dr. Winfried Pohlmeier

Veranstaltungsart: Seminar / Übung

Orga-Einheit: Corporate Management & Economics

Anzeige im Stundenplan: Advanced Methods

Semesterwochenstunden: 1,5

Credits: 3,0

Standort: Campus der Zeppelin Universität

Unterrichtssprache: Englisch

Min. | Max. Teilnehmerzahl: 10 | 35

Inhalte:
The identification and measurement of causal relationship from observational data is a key challenge in the social sciences. In particular, the evaluation of the causal effects (“treatment effects”) of public programs, interventions and other public policies has become increasingly important in recent years. At times of tight government budgets, a thorough empirical analysis of expensive programs is imperative so that causal impact analysis has become a major toolbox in academia and consulting.

The following questions are central for causal impact analysis:
 
“Is the program useful for everybody or just for the participants?
“Would program participation make sense for those who did not participate?”
“Which specific socio-economic group profits most/least from the program?”
“How should a program be redesigned to make it more efficient?”

Course content:

1. Fundamentals of Causal Inference
   
2. Regression methods and inverse probability weighting
   
3. Instrumental Variables and Quasi-Experiments
   
4. Difference-in-Differences
   
5. Regression dicontinuity design
   
6. Causal machine learning methods
   

Lernziele:
The goal of the course is to proide students with a comprehensive overview over the most relevant methods of causal impact analysis.
Students will learn about the pitfalls and opportunities of various causal effects estimation approaches.
Besides a good understanding of the theoretical foundations and their strengths and limitations, students will learn to apply the the tools
to real world economic problems by using R and Quarto.

 

Weitere Informationen zu den Prüfungsleistungen:
Besides the final examination (80% of the final grade) students will work on a case study (20%) using a causal effecs approach. The results will be presented based on a Quarto code book.

Literatur:

• Abadie , A. and M. D. Cattaneo (2018): “Econometric Methods for Program Evaluation,” Annu. Rev. Econ., 10, 465–503.
  
• Angrist , J. D. and J.-S. Pischke (2009): Mostly harmless econometrics: an empiricists companion , Princeton, NJ [u.a.]: Princeton Univ Press.
  
• Chernozhukov, V., C. Hansen, N. Kallus, M. Spindler, and V. Syrgkanis (2024): Applied Causal Inference Powered by ML and AI , https://causalml-book.org/
  
• Wooldridge, J. M. (2010), Econometric Analysis of Cross Section and Panel Data, 2nd. ed., MIT Press: Cambridge, Mass. [u.a.]