Impact Evaluation Methods

The course introduces students to the counterfactual causality model and provides them with a unified framework for answering the questions of cause and effect. It covers the state-of-the-art methods of causal inference such as experimental designs, matching, instrumental variables, regression discontinuity, differences-in-differences and synthetic controls. The course emphasises the intuition behind the methodology rather than formal proofs. It is based on a mixture of textbooks and articulated lessons as well as practical exercises using the most popular software packages.

After completing this module, students will be able to

• build a counterfactual model
• explain the assumptions required to identify a causal effect in experimental and non-experimental designs
• know when to use different causal inference methods
• perform causal inference with existing data sets
• Interpret the results of causal inference

Written term paper of 12 - 15 pages