96359 - DATA SCIENCE FOR POLICY ANALYSIS

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

What is a policy evaluation: ex-ante and ex-post evaluation

Monitory, outcome, output and impact evaluation

Observed, experimental and quasi-experimental data

Steps in evaluation analysis

Counterfactual

Instrumental variables

Difference-in-difference methods

Propensity Score Matching method

Readings/Bibliography

The course is mainly based on:

Gertler, Paul J., Sebastian Martinez, Patrick Premand, Laura B. Rawlings, and Christel M. J. Vermeersch. 2016. Impact Evaluation in Practice, second edition. Washington, DC: Inter-American De velopment Bank and World Bank. doi:10.1596/978-1-4648-0779-4. License: Creative Commons Attribution CC BY 3.0 IGO
Download available at: https://www.worldbank.org/en/programs/sief-trust-fund/publication/impact-evaluation-in-practice

Other useful materials will be provided through the e-learning platform VIRTUALE.

lecture notes and chapters/articles

Potential book(s):

• Rosenbaum, P. (2019). Observation and Experiment: An Introduction to Causal Inference. Place, Harvard University Press, pp.400.
• Rosenbaum, P. (2010). Design of Observational Studies. New York, Springer, pp.384.
• Morgan, S.L., Winship, C. (2011). Counterfactuals and Causal Inference: Methods and Principles for Social Research. Place, Cambridge University Press, pp.328.

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 dataset and will be required to write an essay dealing with the data analysis and result interpretation.

The essay must be carried out exclusively by groups of two students.

It must be submitted by the official day of the exam.

The essay template is available on Virtuale

The vote will take into account the completeness of the analysis, the appropriateness of the terminology used and the clarity of the results.

Teaching tools

Additonal materials (as scientific papers, slides, data..) will be provided during the lessons and the e-learning platform will enable access to this contents.

Office hours

See the website of Meri Raggi

See the website of Silvia De Nicolò