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

arXiv: https://arxiv.org/abs/1912.12864 · HTML: https://mlsanity.com/paper/1912.12864/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Dec 2019.

## Built on by (most cited first, 5 of 5)

- [A Survey of Deep Causal Models and Their Industrial Applications](https://mlsanity.com/paper/2209.08860/) (2022)
- [Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance](https://mlsanity.com/paper/2201.12692/) (2022)
- [Transparency challenges in policy evaluation with causal machine learning -- improving usability and accountability](https://mlsanity.com/paper/2310.13240/) (2023)
- [Causal Machine Learning for Moderation Effects](https://mlsanity.com/paper/2401.08290/) (2024)
- [On the Meta-Design of Allocation Problems](https://mlsanity.com/paper/2602.08786/) (2026)

## Abstract (arXiv)

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.