Double debiased machine learning nonparametric inference with continuous treatments

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-respon...

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Bibliographic Details
Main Authors: Colangelo, Kyle, Lee, Ying-Ying
Format: Report
Language:English
Published: London: Centre for Microdata Methods and Practice (cemmap) 2019
Subjects:
ddc:330
C14
C21
C55
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
DML
Online Access:http://hdl.handle.net/10419/211147
https://doi.org/10.1920/wp.cem.2019.5419
Description
Summary: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.

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