对未测量混杂的简单而敏锐的敏感性分析

IF 1.7 4区 医学 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Journal of Causal Inference Pub Date : 2021-04-27 DOI:10.1515/jci-2021-0041
J. Peña
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引用次数: 4

摘要

摘要:我们提出了一种评估真实因果效应对未测量混杂的敏感性的方法。该方法要求分析人员设置两个直观的参数。否则,该方法是无假设的。该方法返回一个包含真实因果效应的区间,其边界是任意尖锐的,即实际上可以达到的。我们通过实验证明,我们的边界可以比Ding和VanderWeele的方法得到的边界更紧,而且需要比我们的方法多设置一个参数。最后,我们扩展了我们的方法,当存在可测量的介质和未测量的暴露-结果混淆时,将自然的直接和间接影响结合起来。
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Simple yet sharp sensitivity analysis for unmeasured confounding
Abstract We present a method for assessing the sensitivity of the true causal effect to unmeasured confounding. The method requires the analyst to set two intuitive parameters. Otherwise, the method is assumption free. The method returns an interval that contains the true causal effect and whose bounds are arbitrarily sharp, i.e., practically attainable. We show experimentally that our bounds can be tighter than those obtained by the method of Ding and VanderWeele, which, moreover, requires to set one more parameter than our method. Finally, we extend our method to bound the natural direct and indirect effects when there are measured mediators and unmeasured exposure–outcome confounding.
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来源期刊
Journal of Causal Inference
Journal of Causal Inference Decision Sciences-Statistics, Probability and Uncertainty
CiteScore
1.90
自引率
14.30%
发文量
15
审稿时长
86 weeks
期刊介绍: Journal of Causal Inference (JCI) publishes papers on theoretical and applied causal research across the range of academic disciplines that use quantitative tools to study causality.
期刊最新文献
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