Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/207352 
Erscheinungsjahr: 
2019
Schriftenreihe/Nr.: 
IZA Discussion Papers No. 12526
Verlag: 
Institute of Labor Economics (IZA), Bonn
Zusammenfassung: 
Matching-type estimators using the propensity score are the major workhorse in active labour market policy evaluation. This work investigates if machine learning algorithms for estimating the propensity score lead to more credible estimation of average treatment effects on the treated using a radius matching framework. Considering two popular methods, the results are ambiguous: We find that using LASSO based logit models to estimate the propensity score delivers more credible results than conventional methods in small and medium sized high dimensional datasets. However, the usage of Random Forests to estimate the propensity score may lead to a deterioration of the performance in situations with a low treatment share. The application reveals a positive effect of the training programme on days in employment for long-term unemployed. While the choice of the "first stage" is highly relevant for settings with low number of observations and few treated, machine learning and conventional estimation becomes more similar in larger samples and higher treatment shares.
Schlagwörter: 
programme evaluation
active labour market policy
causal machine learning
treatment effects
radius matching
propensity score
JEL: 
J68
C21
Dokumentart: 
Working Paper

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