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Summary This paper reviews, applies, and extends recently proposed methods based on double machine learning (DML) with a focus on programme evaluation under unconfoundedness. DML-based methods leverage flexible prediction models to adjust for confounding variables in the estimation of (a) standard average effects, (b) different forms of heterogeneous effects, and (c) optimal treatment assignment rules. An evaluation of multiple programmes of the Swiss Active Labour Market…

Summary We investigate the finite-sample performance of causal machine learning estimators for heterogeneous causal effects at different aggregation levels. We employ an empirical Monte Carlo study that relies on arguably realistic data generation processes (DGPs) based on actual data in an observational setting. We consider 24 DGPs, eleven causal machine learning estimators, and three aggregation levels of the estimated effects. Four of the considered estimators perform…