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  .
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 (NDR-learner) to address this issue . The NDR-learner acknowledges that individualized effect estimates can be stabilized by an individualized normalization of inverse probability weights .
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(NDR-learner)
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. The NDR-learner acknowledges that individualized effect estimates can be stabilized by an individualized normalization of inverse probability weights