Causal inference with confounders missing not at random
Causal inference with confounders missing not at random
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DOI：
10.1093/biomet/asz048
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发表时间：
2017-02
期刊：
影响因子：
2.7
通讯作者：
Shu Yang;Linbo Wang;Peng Ding
中科院分区：
文献类型：
--
作者：
Shu Yang;Linbo Wang;Peng Ding
从观察性研究中得出因果推断很重要，但如果混杂因素有缺失值，这就变得很有挑战性。一般来说，如果混杂因素缺失而不是随机的，因果效应是不可识别的。在这篇文章中，我们提出了一个新的框架，非参数识别的因果关系的影响与混杂因素受到一个结果独立的missingness，这意味着缺失的数据机制是独立的结果，治疗和可能失踪的混杂因素。然后，我们提出了一个非参数两阶段最小二乘估计和因果效应的参数估计。
It is important to draw causal inference from observational studies, but this becomes challenging if the confounders have missing values. Generally, causal effects are not identifiable if the confounders are missing not at random. In this article we propose a novel framework for nonparametric identification of causal effects with confounders subject to an outcome-independent missingness, which means that the missing data mechanism is independent of the outcome, given the treatment and possibly missing confounders. We then propose a nonparametric two-stage least squares estimator and a parametric estimator for causal effects.