.png){kind=icon}
返回
Marginal and Nested Structural Models Using Instrumental Variables
.png){kind=icon}
PRE
AI
DOI：10.1198/jasa.2009.tm08299.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The objective of many scientific studies is to evaluate the effect of a treatment on an outcome of interest ceterts pat thus In variables (I Vs) serve as an experimental handle. independent of potential outcomes and potential treatment status and affecting potential outcomes only through potential treatment status We propose maternal and nested structural models using IVs. In the spirit of marginal and nested structural models under no unmeasured confounding A marginal structural IV model parameterizes the expectations of two potential outcomes under an active treatment and the null treatment respectively, for those in a covariate-specific subpopulation who would take the act we treatment it the instrument were externally set to each specific level A nested structural IV model parameterizes the difference between the two expectations after transformed by a link function and hence the average treatment effect on the treated at each instrument level We develop IV outcome regression. IV propensity score weighting. and doubly robust methods for estimation. in parallel to those for structural node Is under no unmeasured confounding The regression method requires correctly specified models for the treatment propensity score and the outcome regression function The weighting method requires a correctly specified model for the instrument propensity score The doubly robust estimators depend on the two sets of models and remain consistent if either set of models are correctly specified We apply our methods to study returns to education using data from the National Longitudinal Survey of Young Men
Keyword:
Causal inference
Double robustness
Generalized method of moments
Instrumental variable
Observational study
Propensity score
Structural model
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
J
IF:
3
论文数:
5.2K
被引数:
4.8W
机构
暂无机构信息
.png){kind=icon}
