Jo Booil
Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305, USA.
Psychol Methods. 2008 Dec;13(4):314-36. doi: 10.1037/a0014207.
This article links the structural equation modeling (SEM) approach with the principal stratification (PS) approach, both of which have been widely used to study the role of intermediate posttreatment outcomes in randomized experiments. Despite the potential benefit of such integration, the 2 approaches have been developed in parallel with little interaction. This article proposes the cross-model translation (CMT) approach, in which parameter estimates are translated back and forth between the PS and SEM models. First, without involving any particular identifying assumptions, translation between PS and SEM parameters is carried out on the basis of their close conceptual connection. Monte Carlo simulations are used to further clarify the relation between the 2 approaches under particular identifying assumptions. The study concludes that, under the common goal of causal inference, what makes a practical difference is the choice of identifying assumptions, not the modeling framework itself. The CMT approach provides a common ground in which the PS and SEM approaches can be jointly considered, focusing on their common inferential problems.
本文将结构方程模型（SEM）方法与主分层（PS）方法联系起来，这两种方法都已广泛用于研究随机实验中治疗后中间结果的作用。尽管这种整合有潜在益处，但这两种方法是并行发展的，相互之间几乎没有互动。本文提出了交叉模型转换（CMT）方法，即在PS模型和SEM模型之间来回转换参数估计值。首先，在不涉及任何特定识别假设的情况下，基于PS参数和SEM参数之间紧密的概念联系进行两者之间的转换。蒙特卡罗模拟用于在特定识别假设下进一步阐明这两种方法之间的关系。该研究得出结论，在因果推断的共同目标下，产生实际差异的是识别假设的选择，而不是建模框架本身。CMT方法提供了一个共同基础，在此基础上可以共同考虑PS方法和SEM方法，关注它们共同的推断问题。
