From the aDepartments of Epidemiology and Biostatistics, Harvard School of Public Health, Boston, MA; and bDepartment of Applied Mathematics, Computer Science, and Statistics, University of Ghent, Ghent, Belgium.
Epidemiology. 2014 Mar;25(2):300-6. doi: 10.1097/EDE.0000000000000034.
Methods from causal mediation analysis have generalized the traditional approach to direct and indirect effects in the epidemiologic and social science literature by allowing for interaction and nonlinearities. However, the methods from the causal inference literature have themselves been subject to a major limitation, in that the so-called natural direct and indirect effects that are used are not identified from data whenever there is a mediator-outcome confounder that is also affected by the exposure. In this article, we describe three alternative approaches to effect decomposition that give quantities that can be interpreted as direct and indirect effects and that can be identified from data even in the presence of an exposure-induced mediator-outcome confounder. We describe a simple weighting-based estimation method for each of these three approaches, illustrated with data from perinatal epidemiology. The methods described here can shed insight into pathways and questions of mediation even when an exposure-induced mediator-outcome confounder is present.
方法从因果中介分析已经推广了传统的方法，直接和间接的影响在流行病学和社会科学文献中允许的交互和非线性。然而，方法从因果推理文献本身一直受到一个主要的限制，即所谓的自然直接和间接的影响，这是用来没有从数据中确定每当有一个中介结果混杂因素也受暴露的影响。在这篇文章中，我们描述了三种替代的方法来分解效果，给出了可以解释为直接和间接效应的数量，并且即使在存在暴露诱导的中介结果混杂因素的情况下，也可以从数据中识别出来。我们描述了一个简单的基于权重的估计方法为这三种方法中的每一种，说明与围产期流行病学的数据。这里描述的方法可以洞察途径和调解的问题，即使存在一个暴露诱导的中介结果混杂因素。
