不完全工具变量下的因果推理

IF 1.7 4区 医学 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Journal of Causal Inference Pub Date : 2021-11-04 DOI:10.1515/jci-2021-0065
N. Miklin, M. Gachechiladze, George Moreno, R. Chaves
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引用次数: 4

摘要

工具变量允许在没有干预的情况下对因果关系进行量化。要做到这一点,必须满足一些因果假设,其中最重要的是独立性假设,即工具和任何混淆因素必须是独立的。然而,如果不满足这个独立性条件,我们还能处理不完美的工具变量吗?不完美的工具可以通过违反工具不平等来表现出来,而工具不平等限制了场景中的相关集。在本文中,我们建立了这种违反仪器不等式和最小量的测量依赖之间的定量关系,以解释离散观察变量的情况。因此,我们提供了在仪器场景中存在宽松的测量依赖假设的情况下有效的适应不等式。这允许对具有二元结果的工具情景的平均因果效应的现有和新的下限进行调整。最后,我们在量子力学的背景下讨论我们的发现。
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Causal inference with imperfect instrumental variables
Abstract Instrumental variables allow for quantification of cause and effect relationships even in the absence of interventions. To achieve this, a number of causal assumptions must be met, the most important of which is the independence assumption, which states that the instrument and any confounding factor must be independent. However, if this independence condition is not met, can we still work with imperfect instrumental variables? Imperfect instruments can manifest themselves by violations of the instrumental inequalities that constrain the set of correlations in the scenario. In this article, we establish a quantitative relationship between such violations of instrumental inequalities and the minimal amount of measurement dependence required to explain them for the case of discrete observed variables. As a result, we provide adapted inequalities that are valid in the presence of a relaxed measurement dependence assumption in the instrumental scenario. This allows for the adaptation of existing and new lower bounds on the average causal effect for instrumental scenarios with binary outcomes. Finally, we discuss our findings in the context of quantum mechanics.
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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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