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 .
This paper reviews, applies and extends recently proposed methods based on Double Machine Learning (DML) with a focus on program evaluation under unconfoundedness. DML based methods leverage flexible prediction models to adjust for confounding variables in the estimation of (i) standard average effects, (ii) different forms of heterogeneous effects, and (iii) optimal treatment assignment rules. An evaluation of multiple programs of the Swiss Active Labor Market Policy illustrates how DML based methods enable a comprehensive program evaluation. Motivated by extreme individualized treatment effect estimates of the DR-learner method, we propose the normalized DR-learner to address this issue .
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consolidates recent methodological developments
reviews, applies and extends recently proposed methods
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methods to control for confounding
models to adjust for confounding variables
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We emphasize that these estimators build all on the same doubly robust score, which allows to utilize computational synergies.
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shows
illustrates
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policy analysis. However, we find evidence that estimates of individualized heterogeneous effects can become unstable
program evaluation. Motivated by extreme individualized treatment effect estimates of the DR-learner method, we propose the normalized DR-learner to address this issue