FOSSR Fourth Online Seminar: The FOSSR Policy Learning Platform: A Tutorial

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).