  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 substantial
heterogeneity across programmes and unemployed. Simulations show that
'black-box' rules that reassign unemployed to programmes that maximise
estimated individual gains can considerably improve effectiveness: up to 20
percent more (less) time spent in (un)employment within a 30 months window. A
shallow policy tree delivers a simple rule that realizes about 70 percent of
this gain.
