Double Machine Learning based Program Evaluation under Unconfoundedness

Abstract

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.

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• Analysing a built-in advantage in asymmetric darts contests using causal machine learning
• Regularizing Double Machine Learning in Partially Linear Endogenous Models
• From controlled to undisciplined data: estimating causal effects in the era of data science using a potential outcome framework
• Group Average Treatment Effects for Observational Studies
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