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Autor:innen: 
Erscheinungsjahr: 
2020
Schriftenreihe/Nr.: 
IZA Discussion Papers No. 13051
Verlag: 
Institute of Labor Economics (IZA), Bonn
Zusammenfassung: 
This paper consolidates recent methodological developments based on Double Machine Learning (DML) with a focus on program evaluation under unconfoundedness. DML based methods leverage flexible prediction methods to control for confounding in the estimation of (i) standard average effects, (ii) different forms of heterogeneous effects, and (iii) optimal treatment assignment rules. We emphasize that these estimators build all on the same doubly robust score, which allows to utilize computational synergies. An evaluation of multiple programs of the Swiss Active Labor Market Policy shows how DML based methods enable a comprehensive policy analysis. However, we find evidence that estimates of individualized heterogeneous effects can become unstable.
Schlagwörter: 
causal machine learning
conditional average treatment effects
optimal policy learning
individualized treatment rules
multiple treatments
JEL: 
C21
Dokumentart: 
Working Paper

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