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...
| Main Author: | |
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| Format: | Article in Journal/Newspaper |
| Language: | unknown |
| Published: |
arXiv
2021
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| Subjects: | |
| Online Access: | https://dx.doi.org/10.48550/arxiv.2104.10334 https://arxiv.org/abs/2104.10334 |
| _version_ | 1821499310067417088 |
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| author | Klosin, Sylvia |
| author_facet | Klosin, Sylvia |
| author_sort | Klosin, Sylvia |
| collection | DataCite |
| 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. : 30 pages |
| format | Article in Journal/Newspaper |
| genre | DML |
| genre_facet | DML |
| id | ftdatacite:10.48550/arxiv.2104.10334 |
| institution | Open Polar |
| language | unknown |
| op_collection_id | ftdatacite |
| op_doi | https://doi.org/10.48550/arxiv.2104.10334 |
| op_rights | arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
| publishDate | 2021 |
| publisher | arXiv |
| record_format | openpolar |
| spelling | ftdatacite:10.48550/arxiv.2104.10334 2025-01-16T21:38:46+00:00 Automatic Double Machine Learning for Continuous Treatment Effects Klosin, Sylvia 2021 https://dx.doi.org/10.48550/arxiv.2104.10334 https://arxiv.org/abs/2104.10334 unknown arXiv arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ Econometrics econ.EM Statistics Theory math.ST Machine Learning stat.ML FOS Economics and business FOS Mathematics FOS Computer and information sciences Article CreativeWork article Preprint 2021 ftdatacite https://doi.org/10.48550/arxiv.2104.10334 2022-03-10T14:24:23Z 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. : 30 pages Article in Journal/Newspaper DML DataCite |
| spellingShingle | Econometrics econ.EM Statistics Theory math.ST Machine Learning stat.ML FOS Economics and business FOS Mathematics FOS Computer and information sciences Klosin, Sylvia Automatic Double Machine Learning for Continuous Treatment Effects |
| title | 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_short | Automatic Double Machine Learning for Continuous Treatment Effects |
| title_sort | automatic double machine learning for continuous treatment effects |
| topic | Econometrics econ.EM Statistics Theory math.ST Machine Learning stat.ML FOS Economics and business FOS Mathematics FOS Computer and information sciences |
| topic_facet | Econometrics econ.EM Statistics Theory math.ST Machine Learning stat.ML FOS Economics and business FOS Mathematics FOS Computer and information sciences |
| url | https://dx.doi.org/10.48550/arxiv.2104.10334 https://arxiv.org/abs/2104.10334 |