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FOSSR Webinar: The FOSSR Policy Learning Platform: A Tutorial

Year: 2025

Type: article

Abstract: The folder contains the presentations and abstracts of the FOSSR First Online Seminar dedicated to present a tutorial for the Policy Learning Platform (PLP) a FOSSR tool for learning optimal policies from data allowing for empirical welfare maximization.Implemented by the OPL set of algorithms, the PLP allows to find “treatment assignment rules” that maximize the overall welfare, defined as the sum of the policy effects estimated over all the policy beneficiaries. The PLP allows to learn the optimal policy empirically, i.e. based on data and observations obtained from previous (same or similar) implemented policies. The PLP carries out empirical welfare maximization within three policy classes: (i) Threshold-based; (ii) Linear-combination; and (iii) Decision-tree. We present three implementation of the PLP, using three platforms, i.e. Stata, R and Python. The seminar will be divided into three short persentations by Giovanni Cerulli (CNR-IRCrES), Federico Brogi (ISTAT) and Fabrizio De Fausti (ISTAT), followed by an open conversation with the discussants Marco Ventura (University of Rome ‘La Sapienza’) and Barbara Guardabascio (University of Perugia). Welcome opening by Claudia Pennacchiotti (CNR-IRPPS).

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.14940391

Authors: Cerulli, Giovanni, Brogi, Federico, De Fausti, Fabrizio

Institutions: Research Institute on Sustainable Economic Growth, Istituto di Ricerca sulla Crescita Economica Sostenibile, Istituto Nazionale di Statistica

Language: English

Topic: Advanced Causal Inference Techniques

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W6949372825

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