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Assumptions allowing the estimation of direct causal effects

机译:假设允许估计直接因果效应

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We thank the editor for the opportunity to offer our comments on this ambitious and interesting article investigating the causal relationships between "wealth" and "health". Because our views on what constitutes evidence for causal effects may differ somewhat from the authors' as expressed in this article (which we henceforth refer to as AHMcMR), we begin with a description of a simplified version of their central analyses eschewing causal language, and then summarize (also in a simplified version)the authors' basic causal conclusions. We then offer our view of causal inference and the meaning of direct and indirect causal effects. A simple counterexample is used to show the sense in which AHMcMR's causal conclusions about direct effects may be misleading. The basic issue is an excessive reliance on observed relationships among observed variables, without attendant discussion of justifying assumptions. We do not delve into other statistical issues, such as their use of hot-deck single imputationmethods to deal with missing data.
机译:感谢编辑提供的机会,对这篇关于“财富”与“健康”之间因果关系的雄心勃勃而有趣的文章发表评论。由于我们对构成因果关系证据的观点可能与本文中所表达的作者(我们以下简称为AHMcMR)有所不同,因此,我们从避开因果语言的中央分析的简化版本入手,然后总结(也以简化的形式)作者的基本因果结论。然后,我们提供因果推断的观点以及直接和间接因果效应的含义。一个简单的反例用来说明AHMcMR关于直接影响的因果结论可能会产生误导的意义。基本问题是过度依赖观察变量之间的观察关系,而没有随之而来的关于合理假设的讨论。我们不研究其他统计问题,例如它们使用热甲板单一插补方法来处理丢失的数据。

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