The meeting of the Network for Statistical and Causal Inference (NESCI3) hosted on July 2 at the IMT School for Advanced Studies, to explore a wide range of methodological applications

The NESCI organizing committee, alongside the L'EMbeDS Department of Excellence of the  Sant'Anna School for Advanced Studies and the IMT School for Advanced Studies, announce the upcoming third meeting of the Network for Statistical and Causal Inference (NESCI3). Following the success of NESCI2 in February 2024, NESCI3 will be held on July 2nd, 2024, at the IMT School for Advanced Studies (Aula 2 - Piazza San Francesco 19, Lucca, Italy).

About NESCI

NESCI, founded in 2022, is a network fostering collaboration among researchers from the Sant'Anna School for Advanced Studies, the IMT School for Advanced Studies, the University of Pisa, and Scuola Normale Superiore (SNS). The network focuses on:

• Developing methods applicable across scientific disciplines, with an emphasis on experimental and machine learning approaches.
• Encouraging the exchange of research results among participating institutions.
• NESCI organizes regular events in Pisa and Lucca, with options for remote participation. The network's mission statement can be found here.

NESCI3 meeting

This appointment will comprise talks by the following scholars:

• P. Burauel, "Controlling for discrete unmeasured confounding in nonlinear causal models": the session will discuss a method for addressing unmeasured confounding variables challenges in non-experimental data using a latent variable model.
• F. Serti, "Assessing the heterogeneous impact of economy-wide shocks": the author will present methodology to assess the heterogeneous impacts of economy-wide shocks on firms, like the effect of COVID-19 on exports.
• J. Sprenger, "Causal and counterfactual inference": the talk delves into causal and counterfactual inference, specifically how to extend the framework of the Causal Modeling Semantics (CMS) for evaluating counterfactuals.
• A. Dominici, "The limits and perils of gentle communication against vaccine hesitancy: an informational trial": an in-depth analysis which examines the limitations of Motivational Interviewing (MI) for reducing vaccine hesitancy. While MI improved the perception of vaccines, it surprisingly decreased vaccination willingness.
• F. Chiaromonte, "COVID-19 in Italy: characterizing pre-vaccine epidemic waves through Functional Data Analysis": a study focusing on the analysis of mortality patterns during the first two COVID-19 waves in Italy using Functional Data Analysis. The investigation finds significant differences between the waves and highlights the effectiveness of timely restrictions in curbing mortality.

More details about NESCI3, including the full program, can be found here. 
Participation is free of charge. To secure a seat at the venue or get a remote access link, please register here.

Contact

For inquiries, please contact Prof. Alessio Moneta.