Identification of causal effects with latent confounding and classical additive errors in treatment
Identification of causal effects with latent confounding and classical additive errors in treatment
复制标题
识别治疗中潜在混杂因素和经典加性错误的因果效应
DOI：
10.1002/bimj.201700048
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发表时间：
2018
影响因子：
1.7
通讯作者：
XH Zhou
中科院分区：
文献类型：
--
作者：
Wei Li;Z C Jiang;Zhi Geng;XH Zhou
在本文中，我们讨论了可识别性和估计的因果效应的连续治疗的二元响应时，治疗是测量误差和存在一个潜在的分类混杂与治疗和响应。在一些广泛使用的参数模型下，我们首先讨论了因果效应的可识别性，然后提出了一种估计和推断的方法。我们的方法可以消除由潜在的混杂和测量误差所引起的偏差，只使用一个单一的工具变量。基于识别结果，我们给出了确定潜在分类混杂因素的存在和选择潜在混杂因素的水平数的指导方针。我们将所提出的方法应用于Fracket心脏研究的数据集，以评估收缩压对冠心病的影响。
In this paper, we discuss the identifiability and estimation of causal effects of a continuous treatment on a binary response when the treatment is measured with errors and there exists a latent categorical confounder associated with both treatment and response. Under some widely used parametric models, we first discuss the identifiability of the causal effects and then propose an approach for estimation and inference. Our approach can eliminate the biases induced by latent confounding and measurement errors by using only a single instrumental variable. Based on the identification results, we give guidelines for determining the existence of a latent categorical confounder and for selecting the number of levels of the latent confounder. We apply the proposed approach to a data set from the Framingham Heart Study to evaluate the effect of the systolic blood pressure on the coronary heart disease.