Statistical mediation methods provide valuable information about underlying mediating psychological processes, but the ability to infer that the mediator variable causes the outcome variable is more complex than widely known. Researchers have recently emphasized how violating assumptions about confounder bias severely limits causal inference of the mediator to dependent variable relation. Our article describes and addresses these limitations by drawing on new statistical developments in causal mediation analysis. We first review the assumptions underlying causal inference and discuss three ways to examine the effects of confounder bias when assumptions are violated. We then describe four approaches to address the influence of confounding variables and enhance causal inference, including comprehensive structural equation models, instrumental variable methods, principal stratification, and inverse probability weighting. Our goal is to further the adoption of statistical methods to enhance causal inference in mediation studies.

中文翻译：

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用于增强中介分析中 M 到 Y 关系的因果解释的统计方法。

统计中介方法提供了有关潜在中介心理过程的宝贵信息，但推断中介变量导致结果变量的能力比众所周知的要复杂。研究人员最近强调了违反关于混杂偏见的假设如何严重限制了中介对因变量关系的因果推断。我们的文章通过利用因果中介分析中的新统计发展来描述和解决这些限制。我们首先回顾了因果推断背后的假设，并讨论了在违反假设时检查混杂偏差影响的三种方法。然后，我们描述了四种解决混杂变量影响和增强因果推断的方法，包括综合结构方程模型、工具变量方法、主分层和逆概率加权。我们的目标是进一步采用统计方法来增强中介研究中的因果推断。