  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 Labour Market Policy illustrates how DML based methods enable a
comprehensive program evaluation. Motivated by extreme individualised treatment
effect estimates of the DR-learner, we propose the normalised DR-learner
(NDR-learner) to address this issue. The NDR-learner acknowledges that
individualised effect estimates can be stabilised by an individualised
normalisation of inverse probability weights.
