SingleView of Module
Module (6 Credits)
Causality and Programme Evaluation
- Name in diploma supplement
- Causality and Programme Evaluation
- Responsible
- Admission criteria
- See exam regulations.
- Workload
- 180 hours of student workload, in detail:
- Attendance: 60 hours
- Preparation, follow up: 60 hours
- Exam preparation: 60 hours
- Duration
- The module takes 1 semester(s).
- Qualification Targets
Students taking the course will
- Acquire a sound understanding of identification strategies in microeconometrics
- Gain knowledge of the advantages and limitations of experimental research
- Get familiar with the most important non-experimental techniques and their underlying assumptions
- Learn how to critically assess empirical microeconometric work
- Relevance
For decision makers, e.g. in public policy, it is important to identify causal effects of distinct policy programmes in order to use available resources efficiently. For this purpose there exists a broad variety of methods. This course enables students to critically assess existing empirical evidence and pursue own empirical evaluations.
- Module Exam
In order to pass the course students need to solve and hand in problem sets (20% of the final grade), and to write a term paper (usually 20-30 pages, 80% of the final grade) in which they pursue an own empirical evaluation.
- Usage in different degree programs
- Elements
Lecture with integrated exercise (6 Credits)
Causality and Programme Evaluation
- Name in diploma supplement
- Causality and Programme Evaluation
- Organisational Unit
- Lecturers
- SPW
- 4
- Language
- English
- Cycle
- summer semester
- Participants at most
- no limit
- Preliminary knowledge
Good knowledge of econometrics required.
- Abstract
This is a Master/Ph.D.-level course in causal inference and program evaluation methodology. We will focus on using the potential outcomes approach as a general organizing principle, and examine identification and estimation of treatment effects under various types of assumptions. The course will not go into great depth in regard to any particular applied econometric method, but will instead aim to provide you with enough knowledge about each one to know when, and when not, to use it in empirical work.
- Contents
- Theories of Causation
- Conducting Experiments in Economics
- Randomisation
- Differences-in-Differences
- Instrumental Variables
- Fuzzy DiD / Multiple Testing
- Regression Discontinuity Design
- Methods based on Unconfoundedness
- Quantile Regression
- Evaluating Evaluation Techniques
- Literature
- Angrist & Pischke (2009), Mostly Harmless Econometrics
- Imbens & Wooldridge (2009), "Recent developments in the econometrics of program evaluation". Journal of Economic Literature.
- Teaching concept
Die Veranstaltung entspricht einem Vorlesungsanteil von 2 SWS und einem Übungsanteil von 2 SWS.
- Participants