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70141 - Health Econometrics

Academic Year 2026/2027

                
                        Docente:
                        Elisabetta De Cao
                    
                        Credits:
                        6
                    
                        SSD:
                        ECON-05/A
                    
                        Language:
                        English
                    
                        Teaching Mode:
                        In-person learning (entirely or partially)
                        
                            Campus:
                            Bologna
                        
                            Corso:
                            Second cycle degree programme (LM) in
                            Economics and Public Policy (cod. 6758)

                                    Course Timetable
                                
from Sep 15, 2026 to Oct 15, 2026

Learning outcomes

At the end of the course the students have knowledge of a number of econometric models designed to study the behavior of economic agents using cross sectional and longitudinal data. They are able to critically evaluate the applications of the methods in the health economics field and to perform their own analysis in the context of new case studies.

Course contents

Causal inference and the potential outcomes framework
Treatment effects and program evaluation
Randomized experiments and regression methods
Instrumental variables Difference-in-differences
Regression discontinuity designs
Matching and selection on observables
Applications in health, labor, and public policy

Readings/Bibliography

Main Bibliography

Cunningham, S. (2021). Causal Inference: The Mixtape.
Huntington-Klein, N. (2025). The Effect.
Angrist, J. D., and Pischke, J. S. (2009). Mostly Harmless Econometrics.

Teaching methods

The course will combine lectures, slides, empirical examples, class discussions, quizzes, TA sessions, and practical exercises. Students will work on problem sets and applied examples using statistical software such as R or Stata. Lecture materials, readings, datasets, problem sets, and further course information will be made available through Virtuale.

Assessment methods

The final grade will depend on:

Quizzes in class
Group problem sets
Final exam

More details will be provided on the Virtuale page of the course. The use of AI tools will not be allowed during the final exam.

The maximum possible score is 30 cum laude, awarded when all answers and coursework are correct, complete, and formally rigorous.

The grade is graduated as follows:

<18: failed
18-23: sufficient
24-27: good
28-30: very good
30 e lode: excellent

Teaching tools

Dedicated page on the VIRTUALE platform containing:

News and updated information
Lectures slides
STATA lab material
R lab material

Software STATA: can be installed on students' personal computers (CAMPUS license) and is available at the Computer Lab of the School of Economics and Management.

Software R: can be installed on students' personal computers from the R portal.

Office hours

See the website of Elisabetta De Cao

SDGs

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