Double debiased machine learning nonparametric inference with continuous treatments
We propose a nonparametric inference method for causal e?ects of continuous treatment variables, under unconfoundedness and in the presence of high-dimensional or nonparametric nuisance parameters. Our simple kernel-based double debiased machine learning (DML) estimators for the average dose-respons...
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ftrepec:oai:RePEc:ifs:cemmap:54/19 2024-04-14T08:10:55+00:00 Double debiased machine learning nonparametric inference with continuous treatments Kyle Colangelo Ying-Ying Lee https://www.ifs.org.uk/uploads/CW5419-Double-debiased-machine-learning-nonparametric-inference-with-continuous-treatments.pdf unknown https://www.ifs.org.uk/uploads/CW5419-Double-debiased-machine-learning-nonparametric-inference-with-continuous-treatments.pdf preprint ftrepec 2024-03-19T10:26:46Z We propose a nonparametric inference method for causal e?ects of continuous treatment variables, under unconfoundedness and in the presence of high-dimensional or nonparametric nuisance parameters. Our simple kernel-based double debiased machine learning (DML) estimators for the average dose-response function (or the average structural function) and the partial e?ects are asymptotically normal with a nonparametric convergence rate. The nuisance estimators for the conditional expectation function and the generalized propensity score can be nonparametric kernel or series estimators or ML methods. Using doubly robust in?uence function and cross-?tting, we give tractable primitive conditions under which the nuisance estimators do not a?ect the ?rst-order large sample distribution of the DML estimators. Report DML RePEc (Research Papers in Economics) |
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RePEc (Research Papers in Economics) |
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description |
We propose a nonparametric inference method for causal e?ects of continuous treatment variables, under unconfoundedness and in the presence of high-dimensional or nonparametric nuisance parameters. Our simple kernel-based double debiased machine learning (DML) estimators for the average dose-response function (or the average structural function) and the partial e?ects are asymptotically normal with a nonparametric convergence rate. The nuisance estimators for the conditional expectation function and the generalized propensity score can be nonparametric kernel or series estimators or ML methods. Using doubly robust in?uence function and cross-?tting, we give tractable primitive conditions under which the nuisance estimators do not a?ect the ?rst-order large sample distribution of the DML estimators. |
format |
Report |
author |
Kyle Colangelo Ying-Ying Lee |
spellingShingle |
Kyle Colangelo Ying-Ying Lee Double debiased machine learning nonparametric inference with continuous treatments |
author_facet |
Kyle Colangelo Ying-Ying Lee |
author_sort |
Kyle Colangelo |
title |
Double debiased machine learning nonparametric inference with continuous treatments |
title_short |
Double debiased machine learning nonparametric inference with continuous treatments |
title_full |
Double debiased machine learning nonparametric inference with continuous treatments |
title_fullStr |
Double debiased machine learning nonparametric inference with continuous treatments |
title_full_unstemmed |
Double debiased machine learning nonparametric inference with continuous treatments |
title_sort |
double debiased machine learning nonparametric inference with continuous treatments |
url |
https://www.ifs.org.uk/uploads/CW5419-Double-debiased-machine-learning-nonparametric-inference-with-continuous-treatments.pdf |
genre |
DML |
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DML |
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https://www.ifs.org.uk/uploads/CW5419-Double-debiased-machine-learning-nonparametric-inference-with-continuous-treatments.pdf |
_version_ |
1796308575499321344 |