Department of Data Analysis, Ghent University.
Department of Quantitative Theory and Methods, Emory University.
Perspect Psychol Sci. 2023 Sep;18(5):1254-1266. doi: 10.1177/17456916221134573. Epub 2023 Feb 7.
Mediation analysis prevails for researchers probing the etiological mechanisms through which treatment affects an outcome. A central challenge of mediation analysis is justifying sufficient baseline covariates that meet the causal assumption of no unmeasured confounding. But current practices routinely overlook this assumption. In this article, we suggest a relatively easy way to mitigate the risks of incorrect inferences resulting from unmeasured confounding: include pretreatment measurements of the mediator(s) and the outcome as baseline covariates. We explain why adjusting for pretreatment baseline measurements is a necessary first step toward eliminating confounding biases. We hope that such a practice can encourage explication, justification, and reflection of the causal assumptions underpinning mediation analysis toward improving the validity of causal inferences in psychology research.
中介分析在研究治疗如何影响结果的病因机制方面很流行。中介分析的一个核心挑战是证明有足够的基线协变量满足没有未测量混杂的因果假设。但是，目前的实践通常忽略了这一假设。在本文中，我们建议了一种相对简单的方法来减轻由于未测量混杂而导致不正确推断的风险：将中介和结果的预处理测量作为基线协变量包含在内。我们解释了为什么调整预处理基线测量是消除混杂偏差的必要第一步。我们希望这种做法能够鼓励对中介分析背后的因果假设进行阐明、证明和反思，从而提高心理学研究中因果推断的有效性。
