Funded by: European Commission's Joint Research Centre.
The project develops a causal machine learning framework to evaluate how public policies influence countries’ export specialization, moving beyond prediction to identify which interventions are most effective and under what conditions. By combining detailed policy data from the Global Trade Alert database with trade and country-level indicators, it estimates the causal impact of different policy instruments on the emergence of comparative advantage across products. The project provides policymakers with an evidence-based tool to design, compare, and optimize industrial, trade, and innovation policies, supporting more effective and targeted interventions. Its wider impact lies in advancing the application of explainable AI and causal inference to economic policymaking, enabling more transparent, data-driven decisions that can strengthen competitiveness, foster sustainable economic development, and improve the evaluation of public policy outcomes.
