CME – Causal Methods for Economics

I EDITION | ON SITE | APPLICATION

Deadline for Registration: April 8th, 2026

Period: June 8th – 12th, 2026

Learning objectives 

The Seasonal School aims to strengthen the methodological skills of PhD students and early-career researchers in the field of causal inference in economics, with particular emphasis on techniques for learning causal structures from data, including both panel and time-series settings. Participants will be introduced to a range of modeling approaches, such as directed acyclic graphs, potential outcomes, difference-in-differences, structural vector autoregressions, and local projections. The School further promotes applications to both micro- and macroeconomic contexts, while fostering scientific interaction in an international environment.

Teaching methodologies 

Frontal lectures and tutorials (in R and Matlab).

Target participants 

The School is addressed to PhD students in economics, econometrics, statistics, data science, and related disciplines. Applicants who have not yet commenced their PhD studies, as well as those who have already completed them, are also eligible to apply, subject to the conditions specified in the call (bando).

Coordinator and key teaching staff 

Organizing committee:

• Alessio Moneta, Professor of Economics
• Laura Magazzini, Professor of Econometrics
• Mario Martinoli, Research Fellow in Econometrics

Key teaching staff:

• Scott Cunningham, Baylor University
• Daniel Lewis, University College London
• Federica Russo, Utrecht University and University College London
• Giovanni Ricco, École Polytechnique and University of Warwick (to be confirmed)