- Docente: Meri Raggi
- Credits: 8
- SSD: SECS-S/03
- Language: English
- Moduli: Meri Raggi (Modulo 1) Silvia De Nicolò (Modulo 2)
- Teaching Mode: Traditional lectures (Modulo 1) Traditional lectures (Modulo 2)
- Campus: Bologna
- Corso: First cycle degree programme (L) in Economics, Politics and Social Sciences (cod. 5819)
-
from Feb 11, 2025 to Feb 27, 2025
-
from Feb 28, 2025 to Mar 20, 2025
Learning outcomes
The aim of the course is to build capacity in using data to inform evidence-based decision-making. The course will provide students with a broad overview of tools and methods for data analysis and applied empirical research, with a particular focus on the estimation of causal effect of public policies. Students will analyze real-word datasets and will be guided through case studies from a variety of policy domains. By the end of the course students will be able to perform a basic – yet rigorous – analysis of data to better understand policy choices. They will gain enough data science literacy to interpret and judge the quality of existing empirical research and to communicate the results to decision makers and the public.
Course contents
The main topics are:
- What is a policy evaluation: ex-ante and ex-post evaluation
- Monitoring, outcome, output and impact evaluation
- Observed, experimental and quasi-experimental data
- Steps in conducting an evaluation analysis
- Counterfactual
- Conditioning and instrumental variables
- Regression Discontinuity Design
- Difference-in-difference methods
- Propensity Score Matching method
Note on prerequisites: students are required to have a foundational understanding of the R programming language and basic concepts of simple linear regression models. These prerequisites ensure that participants can effectively engage with the course material and fully benefit from the advanced topics covered.
Readings/Bibliography
The course is mainly based on:
- Gertler, Paul J., Sebastian Martinez, Patrick Premand, Laura B. Rawlings, and Christel M. J. Vermeersch. Impact Evaluation in Practice, second edition. Washington, DC: Inter-American De velopment Bank and World Bank, 2016
Free download available at: https://www.worldbank.org/en/programs/sief-trust-fund/publication/impact-evaluation-in-practice
Other books:
- Angrist, Joshua D., and Jörn-Steffen Pischke. Mastering 'Metrics: The Path from Cause to Effect. Princeton University Press, 2014.
- Cunningham, S. (2021). Causal inference: The mixtape. Yale University Press.
Other useful materials, such as lecture notes and chapters/articles, will be provided through the e-learning platform Virtuale.
Teaching methods
The course consists of a combination of theoretical lectures, applied case studies and quantitative tutorials.
Assessment methods
Despite the course being split into two modules, there is a single grade.
During the course, students will be provided with available datasets and will be required to write an essay dealing with the data analysis and result interpretation.
The essay must be completed solely by groups consisting of one to three students. It must be submitted by the official day of the exam.
The essay template is available on Virtuale.
The grade will take into account the completeness of the analysis, the appropriateness of the terminology used and the clarity of the results.
Teaching tools
Additional materials (such as scientific papers, slides, datasets..) will be provided during the lessons and the e-learning platform Virtuale will enable access to these contents.
Office hours
See the website of Meri Raggi
See the website of Silvia De Nicolò
SDGs

This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.