Selection and integration of generalized instrumental variables for estimating total effects
Selection and integration of generalized instrumental variables for estimating total effects
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DOI：
10.1007/s00362-020-01190-4
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发表时间：
2020-06
影响因子：
1.3
通讯作者：
Ryusei Shingaki;H. Kanda;Manabu Kuroki
中科院分区：
文献类型：
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作者：
Ryusei Shingaki;H. Kanda;Manabu Kuroki
我们考虑的情况下，变量之间的因果关系可以描述为一个有向无环图（DAG）和相应的线性结构方程模型（线性SEM）。当几对工具变量（IV）和协变量（四对;珍珠在：第20届人工智能不确定性会议论文集，AUAI出版社，阿灵顿，弗吉尼亚州，美国，UAI'04，2004），我们提出（i）总效应的IV-对的图形选择准则和（ii）一种综合估计器，将它们结合起来，以更好的精度估计总效应。在本文中，根据Brito和Pearl的论文（在：第18届人工智能不确定性会议论文集，Morgan Kaufmann出版公司，San弗朗西斯科，CA，USA，UAI'02，2002），所提出的估计被称为综合广义工具变量（iGIV）估计。所提出的图形选择标准意味着：（a）即使在治疗和协变量高度相关时，后门标准也比传统的估计总效应的工具变量（IV）方法获得更好的估计准确度;（B）在某些情况下，条件IV方法上级后门标准。iGIV估计量提供了一个通用类，包括基于后门准则的普通最小二乘（OLS）估计量和基于（条件）IV方法的两阶段最小二乘（2SLS）估计量。我们阐明了iGIV估计的性质，其中一些可以从DAG结构中读取。此外，通过数值实验和应用的案例研究，我们表明，iGIV估计的性能是上级的OLS和IV估计。iGIV估计量可以是一个强大的工具，以估计总的效果时，所提出的图形选择标准的IV对不满足。
We consider a situation where cause–effect relationships between variables can be described as a directed acyclic graph (DAG) and the corresponding linear structural equation model (linear SEM). When several pairs of instrumental variables (IVs) and covariates (IV-pairs; Pearl in: Proceedings of the 20th conference on uncertainty in artificial intelligence, AUAI Press, Arlington, Virginia, United States, UAI’04, 2004) are available, we propose (i) the graphical selection criteria of IV-pairs for total effects and (ii) an integrated estimator that combines them to estimate total effects with better accuracy. In this paper, in accordance with the paper by Brito and Pearl (in: Proceedings of the 18th conference on uncertainty in artificial intelligence, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, UAI’02, 2002), the proposed estimator is called an integrated generalized instrumental variable (iGIV) estimator. The proposed graphical selection criteria imply that (a) the back-door criterion achieves better estimation accuracy than the traditional instrumental variable (IV) method of estimating total effects even when the treatment and covariates are highly correlated and (b) the conditional IV method can be superior to the back-door criterion in some situations. The iGIV estimator provides a general class that includes both the ordinary least squares (OLS) estimator based on the back-door criterion and the two-stage least squares (2SLS) estimator based on the (conditional) IV method. We clarify the properties of the iGIV estimator, some of which can be read off from the DAG structure. Furthermore, through numerical experiments and an application to a case study, we show that the performance of the iGIV estimator is superior to those of the OLS and IV estimators. The iGIV estimator can be a powerful tool to estimate the total effect when the proposed graphical selection criteria of the IV-pairs are not satisfied.