JOURNAL OF MULTIVARIATE ANALYSIS | 卷:168 |
Joint sufficient dimension reduction for estimating continuous treatment effect functions | |
Article | |
Huang, Ming-Yueh1  Chan, Kwun Chuen Gary2  | |
[1] Acad Sinica, Inst Stat Sci, Taipei, Taiwan | |
[2] Univ Washington, Dept Biostat, Seattle, WA 98195 USA | |
关键词: Central subspace; Cross-validation; Dose-response; Infinitesimal jackknife; Optimal bandwidth; | |
DOI : 10.1016/j.jmva.2018.07.005 | |
来源: Elsevier | |
【 摘 要 】
The estimation of continuous treatment, effect functions using observational data often requires parametric specification of the effect curves, the conditional distributions of outcomes and treatment assignments given multi-dimensional covariates. While nonparametric extensions are possible, they typically suffer from the curse of dimensionality. Dimension reduction is often inevitable and we propose a sufficient dimension reduction framework to balance parsimony and flexibility. The joint central subspace can be estimated at a n(1/2)-rate without fixing its dimension in advance, and the treatment effect function is estimated by averaging local estimates of a reduced dimension. Asymptotic properties are studied. Unlike binary treatments, continuous treatments require multiple smoothing parameters of different asymptotic orders to borrow different facets of information, and their joint estimation is proposed by a non-standard version of the infinitesimal jackknife. (C) 2018 Elsevier Inc. All rights reserved.
【 授权许可】
Free
【 预 览 】
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