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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\u2019s practical performance.<\/jats:p>","DOI":"10.1093\/jrsssb\/qkad013","type":"journal-article","created":{"date-parts":[[2023,3,31]],"date-time":"2023-03-31T17:03:50Z","timestamp":1680282230000},"page":"474-496","source":"Crossref","is-referenced-by-count":7,"title":["Nonparametric estimation of the continuous treatment effect with measurement error"],"prefix":"10.1093","volume":"85","author":[{"given":"Wei","family":"Huang","sequence":"first","affiliation":[{"name":"School of Mathematics and Statistics, University of Melbourne , Melbourne , Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Center for Applied Statistics, Institute of Statistics & Big Data, Renmin University of China , Beijing , 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