Fabrizia Mealli is on leave as Professor of Statistics from the University of Florence. Since 2021, she has been a Part-time Professor at the EUI's Department of Economics. She held visiting positions at the Harvard Statistics and Biostatistics Departments in 2001, 2015, and 2017.

Her research focuses on statistical and econometric methods for causal inference in experimental and observational settings, estimation techniques, simulation methods, missing data, and Bayesian inference, with applications to the social and biomedical sciences.

Mealli is an Elected Fellow of the American Statistical Association (ASA) and sits on the Steering Committee of the European Causal Inference Meeting (EUROCIM). She is an Associate Editor for Biometrika, the Journal of the American Statistical Association T&M, The Annals of Applied Statistics, and Observational Studies. Mealli is past-President of the Society for Causal Inference. Since 2001, Mealli has been teaching Causal Inference in International Schools and in Master and PhD programmes around the world.

Research projects, clusters and working groups

Ongoing

Estimating causal effects with endogenous networks: a principal stratification approach

Completed

Causal inference under interference and bipartite settings

Supervisees

Fields of expertise

Microeconometrics and causal inference Causality in econometrics Group interactions and network effects Applied econometrics Survey data (design, collection and analysis)

Recent research output

Forastiere L., Mattei A., Mealli F., Selecting Subpopulations for Causal Inference Regression Discontinuity Designs, Annals of Applied Statistics, 19, 3, 1801-1825. https://arxiv.org/abs/2211.09099
Zigler C., Mealli F., Liu V., Forastiere L., Bipartite Interference and Air Pollution Transport: Estimating Health Effects of Power Plant Interventions, Biostatistics, 26, 1, DOI: 10.1093/biostatistics/kxae051
Comment L., Mealli F., Haneuse S., Zigler C.M., Survivor average causal effects for continuous time: a principal stratification approach to causal inference with semicom- peting risks, Biometrical Journal, 67, 2, DOI: 10.1002/bimj.70041

Work in progress

Ichino A., Mealli F., Viviens J. (2026) Do Test Scores Help Teachers Give Better Track Advice to Students? A Principal Stratification Analysis, arXiv: 2511.05128
Viviens J., Mealli F. (2026) Difference-in-Differences under Unknown Interference, under review, arXiv: 2512.21176
Papadogeorgou G, Song Z., Imbens G., Mealli F. (2025), Causal Inference when Intervention Units and Outcome Units Differ, under review, arXiv: 2507.20231
Antonelli J., Wu M., Mealli F., Beck B., Mattei A. (2025) Principal stratification with continuous treatments and continuous post-treatment variables, under review, arXiv: 2309.14486