I study pricing and welfare in pharmaceutical markets by combining causal machine learning (Neyman-orthogonal DML/IV, cross-fitting) with structural IO tools (BLP, multi-product pricing, counterfactuals). Current work tests Ramsey predictions and innovation responses using large, multi-country panels and policy shocks.
Empirical IO & Health: Causal ML for Pharma Pricing
Applied microeconomist (Empirical IO & Health). I study pricing and welfare in pharmaceutical markets by combining causal ML (Neyman-orthogonal DML/IV, cross-fitting) with structural IO (BLP, multi-product pricing) to identify heterogeneous own- and cross-price elasticities and evaluate Ramsey/Boiteux counterfactuals;
Current roles include Research Fellow, IMT;
I was previously Postdoc in Bocconi; Visiting Scholar, MIT Sloan (Mar–Sep 2024), and currently visiting TSE under the supervision of Prof. Dubois (2025).
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Reading group
Machine learning and Econometrics techniques.
IMT School for Advanced Studies
Graduate course: Applied Data Science, Labor Economics
Sant'Anna School for Advanced Studies
Undergraduate course: Advanced Econometrics
Teaching Experience
Education Journey and Academic Background
I trained in economics at Sant’Anna School of Advanced Studies/University of Pisa, completed an M.Sc. in Economics, Networks & Business Analytics at IMT School, and earned a Ph.D. in Economics, Networks & Business Analytics (IMT, 2022) under Massimo Riccaboni and Giorgio Gnecco. My doctoral work emphasized econometrics and causal ML (DML/IV, modern DiD), statistical methods and reproducible visualization, and regulation/policy evaluation, foundations I now apply to Empirical IO and health-market pricing.
2017 - 2022
IMT School for Advanced Studies
Economics Networks Business Analytics
2015- 2017
Sant'Anna School for Advanced Studies
Economics cum Laude
2012 - 2015
University of Pisa
Business Economics cum Laude
Contributions Galore
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Designing panel data that identifies: FEGLS-based rules for the budgeted trade-off between more units and cleaner labels, improving DML identification in panels.
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Predicting city competitiveness: Cluster-then-learn pipeline that raises out-of-sample accuracy and interpretability—transferable to product segmentation and IV construction.
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Modeling spillover dynamics: A network-reinforcement model that maps innovation spillovers—an IO analogue to cross-price interactions and policy propagation
In Bocconi I mainly studies how the process of learning by doing and innovation may tilt and modify traditional demand approaches with a focus on patented innovations (PATSTAT).
Forward into the Future
Navigating the complex juncture of economics, data science, and machine learning, my vision extends beyond the horizon. I aspire to continually bridge the chasm between theoretical economic models and pragmatic applications. Envisage a future where academic collaborations and innovative methodologies redefine the landscape of Economic Research Consulting
What people say about Me !
Massimo Riccaboni
Professor of Economics, IMT School for Advanced Studies Lucca
"Federico is one of the best PhD students in our Economics, Networks, and Business Analytics program. His application of Matrix Completion to assess the complexity of countries, particularly in economic contexts, is both novel and promising. I believe his findings will appeal to academics and policymakers, offering valuable insights into economic complexity."
Giorgio Gnecco
Associate Professor in Operations Research, IMT School for Advanced Studies Lucca
"Federico has impressed me with his scientific curiosity and genuine interest in research. His approach has allowed him to be highly productive in a short period across different topics. I am convinced that he is fully qualified for the position in Applied Economics at Bocconi University."
Step into the reflections of those who have shared this intellectual journey with me, Their words paint a picture of our shared exploration of economic complexities, innovation, and industrial dynamics.