Automatic Double Machine Learning for Continuous Treatment Effects

In this paper, we introduce and prove asymptotic normality for a new nonparametric estimator of continuous treatment effects. Specifically, we estimate the average dose-response function - the expected value of an outcome of interest at a particular level of the treatment level. We utilize tools fro...

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Bibliographic Details
Main Author: Klosin, Sylvia
Format: Text
Language:English
Published: 2021
Subjects:
stat
eco
DML
Online Access:http://arxiv.org/abs/2104.10334
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spelling fttriple:oai:gotriple.eu:10670/1.lu981l 2023-05-15T16:01:43+02:00 Automatic Double Machine Learning for Continuous Treatment Effects Klosin, Sylvia 2021-04-20 http://arxiv.org/abs/2104.10334 en eng 10670/1.lu981l http://arxiv.org/abs/2104.10334 undefined arXiv stat eco Text https://vocabularies.coar-repositories.org/resource_types/c_18cf/ 2021 fttriple 2023-01-22T17:20:06Z In this paper, we introduce and prove asymptotic normality for a new nonparametric estimator of continuous treatment effects. Specifically, we estimate the average dose-response function - the expected value of an outcome of interest at a particular level of the treatment level. We utilize tools from both the double debiased machine learning (DML) and the automatic double machine learning (ADML) literatures to construct our estimator. Our estimator utilizes a novel debiasing method that leads to nice theoretical stability and balancing properties. In simulations our estimator performs well compared to current methods. Comment: 30 pages Text DML Unknown
institution Open Polar
collection Unknown
op_collection_id fttriple
language English
topic stat
eco
spellingShingle stat
eco
Klosin, Sylvia
Automatic Double Machine Learning for Continuous Treatment Effects
topic_facet stat
eco
description In this paper, we introduce and prove asymptotic normality for a new nonparametric estimator of continuous treatment effects. Specifically, we estimate the average dose-response function - the expected value of an outcome of interest at a particular level of the treatment level. We utilize tools from both the double debiased machine learning (DML) and the automatic double machine learning (ADML) literatures to construct our estimator. Our estimator utilizes a novel debiasing method that leads to nice theoretical stability and balancing properties. In simulations our estimator performs well compared to current methods. Comment: 30 pages
format Text
author Klosin, Sylvia
author_facet Klosin, Sylvia
author_sort Klosin, Sylvia
title Automatic Double Machine Learning for Continuous Treatment Effects
title_short Automatic Double Machine Learning for Continuous Treatment Effects
title_full Automatic Double Machine Learning for Continuous Treatment Effects
title_fullStr Automatic Double Machine Learning for Continuous Treatment Effects
title_full_unstemmed Automatic Double Machine Learning for Continuous Treatment Effects
title_sort automatic double machine learning for continuous treatment effects
publishDate 2021
url http://arxiv.org/abs/2104.10334
genre DML
genre_facet DML
op_source arXiv
op_relation 10670/1.lu981l
http://arxiv.org/abs/2104.10334
op_rights undefined
_version_ 1766397467089174528

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