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Your search keyword '"Causal machine learning"' showing total 2 results
Start Over Descriptor "Causal machine learning" Publisher maastricht university, graduate school of business and economics
2 results on '"Causal machine learning"'

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

Author

Cockx, Bart, Cockx, Bart, Lechner, Michael, Bollens, Joost, Cockx, Bart, Cockx, Bart, Lechner, Michael, and Bollens, Joost

Abstract

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.

Published
2020

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

Subjects
Mobility, active labour market policy, Unemployment, modified causal forest, conditional average treatment effects, and Vacancies: Public Policy, causal machine learning, j68 - Mobility, policy evaluation
Abstract

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.

Published
2020

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