Double Machine Learning based Program Evaluation under Unconfoundedness

Michael Knaus

Papers from arXiv.org

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.

Date: 2020-03, Revised 2022-06
New Economics Papers: this item is included in nep-big and nep-cmp
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Citations: View citations in EconPapers (28)

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http://arxiv.org/pdf/2003.03191 Latest version (application/pdf)

Related works:
Working Paper: Double Machine Learning Based Program Evaluation under Unconfoundedness (2020)
Working Paper: Double Machine Learning based Program Evaluation under Unconfoundedness (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2003.03191

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