| Course information | |
|---|---|
| Period | Block 2 |
| Timeline | February |
| Number of ECTS | 5 ECTS |
| Coordinator | Dr Paula Nagler |
| Methodology | Lectures, workshops (hands-on empirical exercises in R) |
Course description
Urban and public policy decisions increasingly require credible evidence on causal effects: whether interventions work, for whom, and under what conditions. Simple correlations are insufficient for answering such questions, particularly in complex urban settings.
This course provides students with training in impact evaluation and causal inference, equipping them with the econometric and research-design tools needed to identify causal effects in applied policy contexts. The course explicitly distinguishes causal inference from descriptive and predictive analysis, highlighting the assumptions required for credible identification.
The course builds on a shared quantitative core – multiple regression, limited dependent variable models, panel data, and instrumental variables – before introducing impact-evaluation methods, including Randomized Control Trials, Regression Discontinuity, Difference-in-Differences, and Matching. Emphasis is placed on research design, identification strategy, and interpretation.
Learning outcomes
After successful completion of the course, students will be able to:
Common learning outcomes (shared with the Machine Learning for Policy track):
- Apply and design sound multivariate econometric models.
- Distinguish clearly between statistical inference, predictive accuracy, and causal inference.
- Communicate quantitative results clearly to non-technical, policy-oriented audiences.
Track-specific learning outcomes:
- Understand causal inference and the counterfactual framework in impact evaluation.
- Apply and critically assess experimental (RCT) and quasi-experimental approaches (RDD, DiD, Matching) for policy evaluation.
Teaching and learning methods
- Lectures
- Workshops (hands-on empirical exercises in R)
The course follows a lecture–workshop format, with workshops closely aligned to lecture content.
Practical exercises
- Applied exercises using real or simulated policy datasets
- Design-based problem sets focusing on identification and assumptions
- Replication of simplified impact-evaluation studies
- Interpretation exercises addressing robustness, validity, and policy relevance
Exercises are formative and directly prepare students for the final assessment.