Double machine learning-based programme evaluation under unconfoundedness
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 a...
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croxfordunivpr:10.1093/ectj/utac015 2024-06-23T07:52:22+00:00 Double machine learning-based programme evaluation under unconfoundedness Knaus, Michael C Swiss National Science Foundation 2022 http://dx.doi.org/10.1093/ectj/utac015 https://academic.oup.com/ectj/advance-article-pdf/doi/10.1093/ectj/utac015/44010461/utac015.pdf https://academic.oup.com/ectj/article-pdf/25/3/602/45842097/utac015.pdf en eng Oxford University Press (OUP) https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model The Econometrics Journal volume 25, issue 3, page 602-627 ISSN 1368-4221 1368-423X journal-article 2022 croxfordunivpr https://doi.org/10.1093/ectj/utac015 2024-06-04T06:12:05Z 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 Policy illustrates how DML-based methods enable a comprehensive programme 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. Article in Journal/Newspaper DML Oxford University Press The Econometrics Journal |
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Oxford University Press |
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croxfordunivpr |
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English |
description |
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 Policy illustrates how DML-based methods enable a comprehensive programme 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. |
author2 |
Swiss National Science Foundation |
format |
Article in Journal/Newspaper |
author |
Knaus, Michael C |
spellingShingle |
Knaus, Michael C Double machine learning-based programme evaluation under unconfoundedness |
author_facet |
Knaus, Michael C |
author_sort |
Knaus, Michael C |
title |
Double machine learning-based programme evaluation under unconfoundedness |
title_short |
Double machine learning-based programme evaluation under unconfoundedness |
title_full |
Double machine learning-based programme evaluation under unconfoundedness |
title_fullStr |
Double machine learning-based programme evaluation under unconfoundedness |
title_full_unstemmed |
Double machine learning-based programme evaluation under unconfoundedness |
title_sort |
double machine learning-based programme evaluation under unconfoundedness |
publisher |
Oxford University Press (OUP) |
publishDate |
2022 |
url |
http://dx.doi.org/10.1093/ectj/utac015 https://academic.oup.com/ectj/advance-article-pdf/doi/10.1093/ectj/utac015/44010461/utac015.pdf https://academic.oup.com/ectj/article-pdf/25/3/602/45842097/utac015.pdf |
genre |
DML |
genre_facet |
DML |
op_source |
The Econometrics Journal volume 25, issue 3, page 602-627 ISSN 1368-4221 1368-423X |
op_rights |
https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model |
op_doi |
https://doi.org/10.1093/ectj/utac015 |
container_title |
The Econometrics Journal |
_version_ |
1802643656184692736 |