Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium

Bart Cockx, Michael Lechner, Joost Bollens

Research output: Working paper / PreprintWorking paper

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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% more (less) time spent in (un)employment within a 30 months window. A shallow policy tree delivers a simple rule that realizes about 70% of this gain.
Original languageEnglish
Place of PublicationMaastricht
Number of pages81
Publication statusPublished - 12 May 2020

Publication series

SeriesROA Research Memoranda

JEL classifications

  • j68 - Mobility, Unemployment, and Vacancies: Public Policy


  • policy evaluation
  • active labour market policy,
  • causal machine learning
  • modified causal forest
  • conditional average treatment effects

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