We estimate heterogeneous wage structure effects for country-pairs within the EU by the Causal Forest algorithm, then identify groups of workers with the highest and lowest discrepancies in terms of wage differentials. We find that, in the East-West comparison, age is the most consistently...

We estimate heterogeneous wage structure effects for country-pairs within the EU by the Causal Forest algorithm, then identify groups of workers with the highest and lowest discrepancies in terms of wage differentials. We find that, in the East-West comparison, age is the most consistently...

This paper consolidates recent methodological developments based on Double Machine Learning (DML) with a focus on program evaluation under unconfoundedness. DML based methods leverage flexible prediction methods to control for confounding in the estimation of (i) standard average effects, (ii)...

Based on administrative data of unemployed in Belgium, we estimate the labour market effects of three training programmes at various aggregation levels using Modified Causal Forests, a causal machine learning estimator. While all programmes have positive effects after the lock-in period, we find...

Based on administrative data of unemployed in Belgium, we estimate the labour market effects of three training programmes at various aggregation levels using Modified Causal Forests, a causal machine learning estimator. While all programmes have positive effects after the lock-in period, we find...