POLICY TARGETING

Policy targeting involves identifying the best recipients and the optimal combination of factors for successful project implementation and achieving specific goals. This is crucial for initiatives like the EU's National Recovery and Resilience Plan (NRRP), where resources need to be used effectively. 

To enhance these efforts, we are tapping into the potential of Machine Learning (ML). In the realm of social sciences, ML techniques are proving invaluable for solving what are known as 'prediction policy problems.' These techniques seek to minimize errors when predicting outcomes, aiming to generalize well to unseen data. They offer promising avenues for assisting policymakers across critical tasks, including early-warning systems, refining targeting rules, and mapping socio-economic outcomes. 

Our approach adopts a sequential forecasting method, leveraging historical data from past projects to optimize the targeting of future measures. By analyzing vast datasets from the EU Cohesion Policy, we aim to develop ML models that can accurately forecast project outcomes and identify key factors driving success. 

Operationally, our ML-based approach involves forecasting project outcomes, identifying influential factors, and developing selection criteria for resource allocation. However, we're mindful of the trade-off between accuracy and interpretability inherent in ML techniques. While complex 'black-box' methods may offer higher accuracy, we prioritize transparency to ensure accountability in public policy decisions. 

In summary, by harnessing the power of ML, we're striving to optimize policy targeting efforts, ultimately contributing to the achievement of NRRP goals and fostering resilience in the face of socio-economic challenges.