In observational data, understanding the causal link when estimating the causal effect of an independent variable (x) on a dependent variable (y) often requires researchers to identify the role of a third variable in the x → y relationship. Mediation, confounding, and colliding are three key third-variable effects that yield different theoretical and methodological implications for drawing causal conclusions. Commonly used covariance-based statistical methods, such as linear regression and structural equation modeling, cannot distinguish these effects in practice, however. In this study, we introduce a statistical approach for distinguishing mediators, confounders, colliders, and potential M-bias structures that uses higher-order moment information from the data. We propose a two-step procedure that uses the Hilbert-Schmidt independence criterion within the direction dependence analysis framework. Results from Monte Carlo simulations show that our proposed approach accurately recovers the true data-generating process of the third variable. We provide an empirical example to demonstrate the application of our proposed approach in psychological research. Finally, we discuss implications and future directions of our work. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

中文翻译：

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一次一步：一种使用方向依赖性分析来区分中介因素、混杂因素和碰撞因素的统计方法。


在观察数据中，在估计自变量 (x) 对因变量 (y) 的因果影响时了解因果联系，通常需要研究人员确定第三个变量在 x → y 关系中的作用。中介、混杂和冲突是三个关键的第三变量效应，它们对于得出因果结论产生不同的理论和方法论意义。然而，常用的基于协方差的统计方法，例如线性回归和结构方程建模，在实践中无法区分这些影响。在本研究中，我们引入了一种统计方法，用于区分中介因素、混杂因素、碰撞因素和潜在的 M 偏差结构，该方法使用数据中的高阶矩信息。我们提出了一个两步程序，在方向依赖性分析框架内使用希尔伯特-施密特独立性准则。蒙特卡罗模拟的结果表明，我们提出的方法准确地恢复了第三个变量的真实数据生成过程。我们提供了一个实证例子来证明我们提出的方法在心理学研究中的应用。最后，我们讨论了我们工作的影响和未来方向。 （PsycInfo 数据库记录 (c) 2023 APA，保留所有权利）。