Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/216363 
Year of Publication: 
2020
Series/Report no.: 
IZA Discussion Papers No. 13051
Publisher: 
Institute of Labor Economics (IZA), Bonn
Abstract: 
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.
Subjects: 
causal machine learning
conditional average treatment effects
optimal policy learning
individualized treatment rules
multiple treatments
JEL: 
C21
Document Type: 
Working Paper

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