On the causal interpretation of randomised interventional indirect effects

IF 3.1 1区数学 Q1 STATISTICS & PROBABILITY

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2023-06-28 DOI:10.1093/jrsssb/qkad066

Caleb H Miles

引用次数: 3

Abstract

Abstract Identification of standard mediated effects such as the natural indirect effect relies on heavy causal assumptions. By circumventing such assumptions, so-called randomised interventional indirect effects have gained popularity in the mediation literature. Here, I introduce properties one might demand of an indirect effect measure in order for it to have a true mediational interpretation. For instance, the sharp null criterion requires an indirect effect measure to be null whenever no individual-level indirect effect exists. I show that without stronger assumptions, randomised interventional indirect effects do not satisfy such criteria. I additionally discuss alternative causal interpretations of such effects.

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关于随机介入间接效应的因果解释

标准中介效应(如自然间接效应)的识别依赖于大量的因果假设。通过规避这些假设，所谓的随机干预间接效应在调解文献中得到了普及。在这里，我介绍了人们可能要求的间接效应测量的性质，以便它有一个真正的中介解释。例如，尖锐零标准要求当不存在个人层面的间接效应时，间接效应度量为零。我的研究表明，如果没有更强有力的假设，随机干预的间接效应就不能满足这些标准。我还讨论了这种影响的其他因果解释。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of the Royal Statistical Society Series B-Statistical Methodology 数学-统计学与概率论

CiteScore

8.80

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： Series B (Statistical Methodology) aims to publish high quality papers on the methodological aspects of statistics and data science more broadly. The objective of papers should be to contribute to the understanding of statistical methodology and/or to develop and improve statistical methods; any mathematical theory should be directed towards these aims. The kinds of contribution considered include descriptions of new methods of collecting or analysing data, with the underlying theory, an indication of the scope of application and preferably a real example. Also considered are comparisons, critical evaluations and new applications of existing methods, contributions to probability theory which have a clear practical bearing (including the formulation and analysis of stochastic models), statistical computation or simulation where original methodology is involved and original contributions to the foundations of statistical science. Reviews of methodological techniques are also considered. A paper, even if correct and well presented, is likely to be rejected if it only presents straightforward special cases of previously published work, if it is of mathematical interest only, if it is too long in relation to the importance of the new material that it contains or if it is dominated by computations or simulations of a routine nature.