The following competences must be acquired to start the course 'Causal inference'. The appolication of:

- Linear Regression with multiple variables:Omitted variable bias, multiple regressionmodel, multicollinearity
- Non-linear regression analysis: Non-linearregression function, log-linear models,interaction terms, non-linear least squares estimator
- Panel data regression analysis: Fixed effects
- Regression with a binary dependent variable:Linear probability model, probit, logit

This concerns a transition curriculum. No contact sessions are provided. The student is only required to participate in the evaluation.

The primary goal is to provide students with a solid theoretical and practical basis for (1) the interpretation of empirical economic studies and (2) independently solving economic issues using quantitative causal inference techniques. Each lecture addresses a specific quantitative method, which is subsequently applied to different macro- and micro-economic topics. There are interactive computer sessions that enables students to work with datasets related to the topics discussed throughout the course. At the end of the course, the student should be able to perform empirical analyses independently and to critically evaluate economic studies. The focus is on randomized controlled trials, difference-in-differences, instrumental variables and regression discontinuity.