Double/Debiased/Neyman Machine Learning of Treatment Effects

Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, and Newey (2016) provide a generic double/de-biased machine learning (DML) approach for obtaining valid inferential statements about focal parameters, using Neyman-orthogonal scores and cross-fitting, in settings where nuisance parameters are estima...

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Main Authors: Chernozhukov, Victor, Chetverikov, Denis, Demirer, Mert, Duflo, Esther, Hansen, Christian, Newey, Whitney
Format: Report
Language:unknown
Published: arXiv 2017
Subjects:
Machine Learning stat.ML
Methodology stat.ME
FOS Computer and information sciences
DML
Online Access:https://dx.doi.org/10.48550/arxiv.1701.08687
https://arxiv.org/abs/1701.08687
id ftdatacite:10.48550/arxiv.1701.08687
record_format openpolar
spelling ftdatacite:10.48550/arxiv.1701.08687 2023-05-15T16:01:51+02:00 Double/Debiased/Neyman Machine Learning of Treatment Effects Chernozhukov, Victor Chetverikov, Denis Demirer, Mert Duflo, Esther Hansen, Christian Newey, Whitney 2017 https://dx.doi.org/10.48550/arxiv.1701.08687 https://arxiv.org/abs/1701.08687 unknown arXiv arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ Machine Learning stat.ML Methodology stat.ME FOS Computer and information sciences Preprint Article article CreativeWork 2017 ftdatacite https://doi.org/10.48550/arxiv.1701.08687 2022-04-01T10:48:08Z Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, and Newey (2016) provide a generic double/de-biased machine learning (DML) approach for obtaining valid inferential statements about focal parameters, using Neyman-orthogonal scores and cross-fitting, in settings where nuisance parameters are estimated using a new generation of nonparametric fitting methods for high-dimensional data, called machine learning methods. In this note, we illustrate the application of this method in the context of estimating average treatment effects (ATE) and average treatment effects on the treated (ATTE) using observational data. A more general discussion and references to the existing literature are available in Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, and Newey (2016). : Conference paper, forthcoming in American Economic Review, Papers and Proceedings, 2017. arXiv admin note: text overlap with arXiv:1608.00060 Report DML DataCite Metadata Store (German National Library of Science and Technology)
institution Open Polar
collection DataCite Metadata Store (German National Library of Science and Technology)
op_collection_id ftdatacite
language unknown
topic Machine Learning stat.ML
Methodology stat.ME
FOS Computer and information sciences
spellingShingle Machine Learning stat.ML
Methodology stat.ME
FOS Computer and information sciences
Chernozhukov, Victor
Chetverikov, Denis
Demirer, Mert
Duflo, Esther
Hansen, Christian
Newey, Whitney
Double/Debiased/Neyman Machine Learning of Treatment Effects
topic_facet Machine Learning stat.ML
Methodology stat.ME
FOS Computer and information sciences
description Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, and Newey (2016) provide a generic double/de-biased machine learning (DML) approach for obtaining valid inferential statements about focal parameters, using Neyman-orthogonal scores and cross-fitting, in settings where nuisance parameters are estimated using a new generation of nonparametric fitting methods for high-dimensional data, called machine learning methods. In this note, we illustrate the application of this method in the context of estimating average treatment effects (ATE) and average treatment effects on the treated (ATTE) using observational data. A more general discussion and references to the existing literature are available in Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, and Newey (2016). : Conference paper, forthcoming in American Economic Review, Papers and Proceedings, 2017. arXiv admin note: text overlap with arXiv:1608.00060
format Report
author Chernozhukov, Victor
Chetverikov, Denis
Demirer, Mert
Duflo, Esther
Hansen, Christian
Newey, Whitney
author_facet Chernozhukov, Victor
Chetverikov, Denis
Demirer, Mert
Duflo, Esther
Hansen, Christian
Newey, Whitney
author_sort Chernozhukov, Victor
title Double/Debiased/Neyman Machine Learning of Treatment Effects
title_short Double/Debiased/Neyman Machine Learning of Treatment Effects
title_full Double/Debiased/Neyman Machine Learning of Treatment Effects
title_fullStr Double/Debiased/Neyman Machine Learning of Treatment Effects
title_full_unstemmed Double/Debiased/Neyman Machine Learning of Treatment Effects
title_sort double/debiased/neyman machine learning of treatment effects
publisher arXiv
publishDate 2017
url https://dx.doi.org/10.48550/arxiv.1701.08687
https://arxiv.org/abs/1701.08687
genre DML
genre_facet DML
op_rights arXiv.org perpetual, non-exclusive license
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
op_doi https://doi.org/10.48550/arxiv.1701.08687
_version_ 1766397554451283968

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