Program

CIML Training & Conference "Frontiers of Causal Inference and Machine Learning"

April 22-24, 2026

Scuola IMT Alti Studi Lucca

Wednesday, April 22, 2026

13:30 – 14:00: Registration 

14:00 – 14:30: Institutional greetings

14:30 – 16:00: Keynote talk by Richard Hendra (MDRC, New York). "Towards Personalized Policy: From Subgroup Analysis to Machine Learning for Heterogeneous Treatment Effects". Chair: Armando Rungi (IMT School Lucca)

16;00 – 16;30 - Coffee break

16:30 – 18:30 - Session 1. Methodological Advances in Causal Inference and Machine Learning. Chair: Kenan Huremovic (IMT School Lucca)

• Riccardo Di Francesco (University of Southern Denmark)
  A Unified Framework for Validating Machine Learning Predictions and Heterogeneous Treatment Effects.

• Riccardo Cadei (Causal Learning and Artificial Intelligence Lab Vienna)
  Neural Effect Modifier Search.

• Giulio Grossi (University of Florence)
  Climate-risk Resilience and Public Support for Mitigation: a Spatial Latent-Factor Approach to Causal Inference.

• Michele Lenza (European Central Bank)
  Word2Prices: Embedding Central Bank Communications for Inflation Prediction

18:30 – 19:30: Alumni plenary talk by Falco Bargagli Stoffi (University of California, Los Angeles). Who Benefits from a Treatment? Statistically Principled Discovery of Treatment Effect Modifiers via Causal Machine Learning.

Chair: Armando Rungi (IMT School Lucca)

Thursday, April 23, 2026

09:00 – 10:30. Session 2. Causal Inference, Policy Evaluation, and the Evolution of Economic Methodology. Chair: Giovanni Cerulli (CNR-IRCRES)

• Marco Sforza (Roma Tre University, Dept. of Economics)
  One Size Fits All? Optimal Policy Learning for Technology Diffusion in Heterogeneous Context.

• Franco Peracchi (EIEF & Tor Vergata University of Rome)
  A Local Differencing Test for the Credibility of Selection-on-Observables.

• Luca Alfieri (Politecnico di Milano & University of Tartu)
  Methodological Nationalism and Cosmopolitanism in Economics: A Study with a Large Language Model.

10:30 – 11:00 – Coffee break

11:00 – 12:30 – Session 3. PhD Students’ Invited Session. Economic Applications of Machine Learning and Causal Inference. Chair: Marianna Marino (IMT Lucca)

• Maria Cristina Maurizio (IMT School Lucca)
  Machine Learning for Profiling in the Italian Public Employment System.

• Michele Liberatore (IMT School Lucca)
  Licensing and Innovation Regimes in Pharmaceutical R&D.

• Giang Vu Le (IMT School Lucca)
  Picking the Winners. Subsidies under Optimal Policy Learning.

12:30 – 14:00 - Lunch

14:00 – 15:30 – Teaching session 1 by Giovanni Cerulli (CNR-IRCRES): Optimal Policy Learning

15:30 – 16;00 – Coffee break

16;00 - 18:00 – Session 4. Sentiment Analysis and Text Mining for Macroeconomic Forecasting. Chair: Antonio Zinilli

• Luigi Longo (European Commission)
  Shocks from the media and the effect on macroeconomic expectations.

• Paul Soto (Federal Reserve Board)
  Manufacturing Uncertainty: Building Uncertainty Measures from Firm-level Survey.

• Juri Marcucci (Bank of Italy)
  Reddit's Pulse on US Inflation: Forecasting with Large Language Models.

18:00 – 19:30 – Session 5. FOSSR (Fostering Open Science in Social Research). Chair: Fabio Longo

• Giusy Giulia Tuccari (CNR)
  LEAD: LLM-enhanced Engine for Author Disambiguation

• Rocco Paolillo (CNR) Integrating knowledge graphs and multi-agent systems: from
  Integrating Knowledge graphs and multiagent systems: from individual decisions to collective dynamics

• Fabrizio Pecoraro (CNR)
  Integrating causal machine learning and agent-based modelling: an application in the healthcare domain.

20:00 – Social dinner at Restaurant Il Mecenate

Friday, April 24, 2026

09:00 – 10:30 – Session 6. Machine Learning and AI for Economic Measurement and Prediction. Chair: Marianna Marino

·         Adriano Amati (ETH Zürich)
Graph Learning for Corporate Infiltration Risk.

·         Riccardo Di Francesco (University of Southern Denmark)
Measuring Firm Engagement with Employee Well-Being and Mental Health using Corporate Disclosures.

·         Werner Hernani-Limarino (Fundación ARU)
Optimal Poverty Prediction Under Measurement Error: Theory and Evidence from Augmented Machine Learning.

10:30 –11:00 – Coffee break

11:00 – 12:30 – Keynote talk by Fabrizia Mealli (European University Institute). “Do Test Scores Help Teachers Give Better Track Advice to Students? A Principal Stratification Analysis”. Chair: Giovanni Cerulli (CNR-IRCRES)

12:30 – 14:00 - Lunch

14:00 – 15:00 – Teaching session 2 by Armando Rungi (IMT Lucca). Machine Learning with Firm-Level data.

15:00 – 15:15 - Farewell