Statistics and Data Science I (19140) Statistics and Data Science II (19141)

- Ability to describe the logic of causal inference and its application to regression models, distinguishing between causality and correlation. - Ability to identify common problems of causal interpretation in linear models, as well as to evaluate and justify techniques to solve them. - Ability to evaluate the validity and robustness of causal inference under a variety of assumptions about data generation.

1. Why causality? Introduction to the potential outcome frameworks 2. The experimental benchmark 3. Observational data and the experimental benchmark: Matching 4. Quasi-experiments: Regression Discontinuity Designs 5. Quasi-experiments: Instrumental variables 6. Difference-in-differences and panel data 7. Causal inference applied: Policy evaluations

Training Activities: - Theoretical-practical classes - Group work - Individual student work Teaching Methods: - Presentations in the professor's lecture room with computer and audiovisual support, in which the main concepts of the subject are developed and a bibliography is provided to complement the students' learning. - Critical reading of texts recommended by the subject professor: Press articles, reports, manuals and/or academic articles, either for later discussion in class, or to expand and consolidate knowledge of the subject. - Resolution of practical cases, problems, etc. raised by the professor, either individually or in a group - Presentation and discussion in class, under the moderation of the professor, of topics related to the content of the subject, as well as practical case studies. - Developing pieces of work and reports, individually or in group.