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70141 - Health Econometrics

Academic Year 2025/2026

                
                        Docente:
                        Pietro Biroli
                    
                        Credits:
                        6
                    
                        SSD:
                        SECS-P/05
                    
                        Language:
                        English
                    
                        Teaching Mode:
                        Traditional lectures
                        
                            Campus:
                            Bologna
                        
                            Corso:
                            Second cycle degree programme (LM) in
                            Economics and Public Policy (cod. 5945)

                                    Course Timetable
                                
from Sep 16, 2025 to Oct 16, 2025

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

The focus will be on theoretical discussion of causal inference and estimation of treatment effects. We will first discuss a general causal model using the counterfactual outcomes framework; we will then go through the main methods available to the empirical researcher to estimate causal parameters. We will stress how these methods require different assumptions about the assignment mechanism, i.e. the mechanism used to assign individuals to the treatment. We start with the assumption that assignment is (conditionally) random, and discuss suitable methods such as matching and OLS regression. We will then consider a confounded assignment mechanism, and discuss instrumental variable analysis (IV) as well as regression discontinuity design (RDD), which exploits changes in the probability of receiving treatment due to discontinuities in the assignment mechanism. Finally, we will discuss methods which rely on longitudinal information to recover causal parameters

Readings/Bibliography

Introductory Econometrics: A Modern Approach, 2019, 7th Edition, Jeffrey M. Wooldridge.

Causal Inference: The Mixtape. Yale University Press, 2021. Cunningham, Scott. https://mixtape.scunning.com/

Blundell, R., and M. Costa Dias (2009). "Alternative Approaches to Evaluation in Empirical Microeconomics." Journal of Human Resources, 44(3), 565-640.

Imbens, G., and J.F. Wooldridge (2009). "Recent Developments in the Econometrics of Program Evaluation." Journal of Economic Literature, 47(1): 5-8

Athey, S., and Imbens, G. W. (2017). "The State of Applied Econometrics: Causality and Policy Evaluation." Journal of Economic Perspectives, 31(2), 3–32.

Teaching methods

Face-to-face lectures based on professor's slides.

In class tutorials with hands-on practical examples.

Assessment methods

The final grade will depend on:

Quizzes in class
Group problem sets
Final exam

More details will be provided in the Virtuale page of the course.

The maximum possible score is 30 cum laude, in case all answers/course works 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

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: freely available online.

Office hours

See the website of Pietro Biroli