Double Machine Learning based Program Evaluation under Unconfoundedness

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 effe...

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Main Author: Knaus, Michael C.
Format: Text
Language:unknown
Published: 2020
Subjects:
stat
manag
DML
Online Access:http://arxiv.org/abs/2003.03191
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spelling fttriple:oai:gotriple.eu:10670/1.bn4nlo 2023-05-15T16:01:17+02:00 Double Machine Learning based Program Evaluation under Unconfoundedness Knaus, Michael C. 2020-03-06 http://arxiv.org/abs/2003.03191 undefined unknown 10670/1.bn4nlo http://arxiv.org/abs/2003.03191 undefined arXiv stat manag Text https://vocabularies.coar-repositories.org/resource_types/c_18cf/ 2020 fttriple 2023-01-22T17:45:02Z 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. Text DML Unknown
institution Open Polar
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op_collection_id fttriple
language unknown
topic stat
manag
spellingShingle stat
manag
Knaus, Michael C.
Double Machine Learning based Program Evaluation under Unconfoundedness
topic_facet stat
manag
description 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.
format Text
author Knaus, Michael C.
author_facet Knaus, Michael C.
author_sort Knaus, Michael C.
title Double Machine Learning based Program Evaluation under Unconfoundedness
title_short Double Machine Learning based Program Evaluation under Unconfoundedness
title_full Double Machine Learning based Program Evaluation under Unconfoundedness
title_fullStr Double Machine Learning based Program Evaluation under Unconfoundedness
title_full_unstemmed Double Machine Learning based Program Evaluation under Unconfoundedness
title_sort double machine learning based program evaluation under unconfoundedness
publishDate 2020
url http://arxiv.org/abs/2003.03191
genre DML
genre_facet DML
op_source arXiv
op_relation 10670/1.bn4nlo
http://arxiv.org/abs/2003.03191
op_rights undefined
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