Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/211147
Authors: 
Colangelo, Kyle
Lee, Ying-Ying
Year of Publication: 
2019
Series/Report no.: 
cemmap working paper No. CWP54/19
Abstract: 
We propose a nonparametric inference method for causal effects 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 effects 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 influence function and cross-fitting, we give tractable primitive conditions under which the nuisance estimators do not affect the first order large sample distribution of the DML estimators.
Subjects: 
Average structural function
continuous treatment
cross-fitting
dose-response function
double debiased machine learning
doubly robust
high dimension
nonseparable models
partial mean
post-selection inference
JEL: 
C14
C21
C55
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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