  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.
