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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Bibliographic Details
Main Author: Klosin, Sylvia
Format: Article in Journal/Newspaper
Language:unknown
Published: arXiv 2021
Subjects:
Econometrics econ.EM
Statistics Theory math.ST
Machine Learning stat.ML
FOS Economics and business
FOS Mathematics
FOS Computer and information sciences
DML
Online Access:https://dx.doi.org/10.48550/arxiv.2104.10334
https://arxiv.org/abs/2104.10334
Description
Summary: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. : 30 pages

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