Causal Inference

This course covers the microeconometric approach to causality, centred on the Rubin causal model, and the macroeconometric approach, based on intervention analysis. Accordingly, the following topics will take centre stage: panel data analysis, differences-in-differences specifications, regression discontinuity designs and dynamic causal analysis.
The course begins by providing a concise yet thorough review of the multiple linear regression model and of stationary time series analysis.

By the end of the course participants will have gained a sound understanding of how causal inference can be conducted in modern statistics and econometrics. They will be able to follow the state-of-the-art literature and apply the techniques to empirical econometric analyses of their own.

The basics of the material covered in this course will be taken from Stock & Watson(2015). More advanced reading will be suggested at the beginning of the course.

The material will be presented in the lectures in a both qualitative and quantitative
manner. Computer simulations, empirical illustrations as well as exercises will supplement
informal discussions.

Each participant is requested to work on a research topic and present his/her results in a 30-minute talk in the last session of the course. The homework assignment as well as the presentation will be carried out individually. A 3- to 5-page report must be submitted one week prior to the presentation and will be disseminated amongst participants. The topic can be theoretical, empirical or simulation-based. It is important that both report and presentation reflect the fact that this is a course in econometrics. That is to say, the emphasis of the treatment should lie on
• a clear exposition of the econometric model,
• a detailled description of the econometric methods, and
• a critical econometric discussion of the results. Standard econometric notation should be used throughout. Computer code and empirical data should be made available, e.g. by attaching appropriate files to the pdf document of the report.

Familiarity with basic probability and statistical theory is assumed, as well as of the essentials of regression and time series analysis. The text by Stock & Watson (2015) is used throughout this course, and a revision of its chapters 2 to 7 and 14 is highly recommended as a preparation.