DeepAI AI Chat
Log In Sign Up

Nonparametric Estimation of the Continuous Treatment Effect with Measurement Error

by   Wei Huang, et al.
Renmin University of China
The University of Melbourne

We identify the average dose-response function (ADRF) for a continuously valued error-contaminated treatment by a weighted conditional expectation. We then estimate the weights nonparametrically by maximising a local generalised empirical likelihood subject to an expanding set of conditional moment equations incorporated into the deconvolution kernels. Thereafter, we construct a deconvolution kernel estimator of ADRF. We derive the asymptotic bias and variance of our ADRF estimator and provide its asymptotic linear expansion, which helps conduct statistical inference. To select our smoothing parameters, we adopt the simulation-extrapolation method and propose a new extrapolation procedure to stabilise the computation. Monte Carlo simulations and a real data study illustrate our method's practical performance.


page 1

page 2

page 3

page 4


Estimation of Partially Conditional Average Treatment Effect by Hybrid Kernel-covariate Balancing

We study nonparametric estimation for the partially conditional average ...

Kernel Smoothing of the Treatment Effect CDF

We provide a CV-TMLE estimator for a kernel smoothed version of the cumu...

On IPW-based estimation of conditional average treatment effect

The research in this paper gives a systematic investigation on the asymp...

Collaborative targeted minimum loss inference from continuously indexed nuisance parameter estimators

Suppose that we wish to infer the value of a statistical parameter at a ...

A nonparametric doubly robust test for a continuous treatment effect

The vast majority of literature on evaluating the significance of a trea...

Automatic Double Machine Learning for Continuous Treatment Effects

In this paper, we introduce and prove asymptotic normality for a new non...

The Delta-Method and Influence Function in Medical Statistics: a Reproducible Tutorial

Approximate statistical inference via determination of the asymptotic di...