Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/241875 
Year of Publication: 
2019
Series/Report no.: 
cemmap working paper No. CWP72/19
Publisher: 
Centre for Microdata Methods and Practice (cemmap), London
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 nonparametric convergence rates. The nuisance estimators for the conditional expectation function and the conditional density 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. We implement various ML methods in Monte Carlo simulations and an empirical application on a job training program evaluation to support the theoretical results and demonstrate the usefulness of our DML estimator in practice.
Subjects: 
Average structural function
cross-fitting
dose-response function
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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