Giulio Grossi
Giulio Grossi
Post-Doc in Statistics @University of Florence
Hi! I'm a Ph.D in Statistics & Data Science at the University of Florence, advised by prof. Alessandra Mattei. Currently, I am also working with prof. Emilia Rocco to develop novel methods to assess socio-economic performances.
Prior to the Ph.D., I graduated summa cum laude in Economics at the University of Florence, have joined the CINTURS in Faro, PT, and worked as research fellow in IRPET - Istituto Regionale per la Programmazione Economica Toscana, in Florence, IT.
My research spans focuses on Causal Inference themes in non-trivial settings, such as longitudinal studies, interference between units and optimal policy learning.
In God we trust, all others brings data - W.E.Deming
Research Statement
Research Statement
I have always considered it crucial for economic theory to describe reality, and I have always looked to real data to explain phenomena.
I have always considered it crucial for economic theory to describe reality, and I have always looked to real data to explain phenomena.
My research interests span the field of causal inference in longitudinal, spatial, and network contexts. In particular, I am currently working on Synthetic Control Method and Matrix Completion estimators. I am interested in working in contexts of inter-unit interference, treatment diffusion, and optimal policy learning. I am also interested in working in non-traditional policy evaluation contexts, e.g.: without control units, with full interference between units, with clustering observations. I think these kinds of applications, although very challenging, are representative of the real world.
My research interests span the field of causal inference in longitudinal, spatial, and network contexts. In particular, I am currently working on Synthetic Control Method and Matrix Completion estimators. I am interested in working in contexts of inter-unit interference, treatment diffusion, and optimal policy learning. I am also interested in working in non-traditional policy evaluation contexts, e.g.: without control units, with full interference between units, with clustering observations. I think these kinds of applications, although very challenging, are representative of the real world.
Nonetheless, I am very interested in the intersection of the econometric literature and causal inference, which I believe to be one of the major areas of development in econometrics over the next 10 years, along with applications of Bayesian statistics in econometrics, and applied machine and statistical learning.
Nonetheless, I am very interested in the intersection of the econometric literature and causal inference, which I believe to be one of the major areas of development in econometrics over the next 10 years, along with applications of Bayesian statistics in econometrics, and applied machine and statistical learning.
Applications for these methods range from fiscal, health, transportation, energy, and economic policies. I'd like to think of my work as a tool for enhancing the experiences of citizenship, helping to provide tools capable of improving policies.
Applications for these methods range from fiscal, health, transportation, energy, and economic policies. I'd like to think of my work as a tool for enhancing the experiences of citizenship, helping to provide tools capable of improving policies.