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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| Online Access: | http://arxiv.org/pdf/2104.10334 |
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ftrepec:oai:RePEc:arx:papers:2104.10334 2024-04-14T08:10:55+00:00 Automatic Double Machine Learning for Continuous Treatment Effects Sylvia Klosin http://arxiv.org/pdf/2104.10334 unknown http://arxiv.org/pdf/2104.10334 preprint ftrepec 2024-03-19T10:40:23Z 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. Report DML RePEc (Research Papers in Economics) |
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Open Polar |
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RePEc (Research Papers in Economics) |
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ftrepec |
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unknown |
| 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. |
| format |
Report |
| author |
Sylvia Klosin |
| spellingShingle |
Sylvia Klosin Automatic Double Machine Learning for Continuous Treatment Effects |
| author_facet |
Sylvia Klosin |
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Sylvia Klosin |
| 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 |
| url |
http://arxiv.org/pdf/2104.10334 |
| genre |
DML |
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DML |
| op_relation |
http://arxiv.org/pdf/2104.10334 |
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1796308585729228800 |