Standard structural equation models (SEMs) are often used to identify latent mediators. However, valid inference typically relies on the strong, frequently violated Sequential Ignorability assumption. We introduce the Rank-Preserving Structural Equation Model (RAPSEM), which increases robustness through G-estimation while maintaining the measurement model's integrity through a two-stage method of moments (2SMM) for factor score corrections. RAPSEM replaces the no unmeasured mediator-outcome confounding with the weaker no unobserved effect modification assumption. By leveraging treatment randomization, RAPSEM achieves identification in a manner equivalent to instrumental variable estimation through structurally emerging instruments. Specifically, identification relies on treatment-covariate interactions that influence the mediator but have no direct effect on the outcome, allowing researchers to utilize natural heterogeneity in treatment response as a testable source of identification. We provide a robustness assessment for the core identifying assumption and establish the consistency and asymptotic normality of the resulting estimator. Simulation studies demonstrate that RAPSEM remains unbiased under unobserved confounding, whereas standard SEM yields biased results. RAPSEM achieves reasonable power for sample sizes above 500, depending on the strength of the structural instruments. The method is implemented in the accompanying rapsem R package, and its practical utility is illustrated through an empirical example from educational research. The code is available at https://github.com/PsychometricsMZ/RAPSEM.

翻译：摘要：标准结构方程模型(Structural Equation Models, SEM)常被用于识别潜在中介变量。然而，有效推断通常依赖于强且常被违反的“序贯可忽略性”(Sequential Ignorability)假设。我们提出秩保留结构方程模型(Rank-Preserving Structural Equation Model, RAPSEM)，该模型通过G估计(G-estimation)增强稳健性，同时采用两阶段矩估计法(Two-Stage Method of Moments, 2SMM)进行因子得分校正，以维护测量模型的完整性。RAPSEM将“无未测量中介-结果混杂”假设替换为更弱的“无未观察效应修饰”(No Unobserved Effect Modification)假设。通过利用治疗随机化，RAPSEM以等价于工具变量估计的方式，借助结构涌现工具变量实现识别。具体而言，识别依赖于影响中介变量但对结果无直接效应的治疗-协变量交互作用，使研究者能够利用治疗反应的自然异质性作为可检验的识别来源。我们为核心识别假设提供了稳健性评估，并建立了所得估计量的一致性与渐近正态性。模拟研究表明，在存在未观测混杂的情况下，RAPSEM仍保持无偏性，而标准SEM产生有偏结果。在样本量超过500时（取决于结构工具变量的强度），RAPSEM可获得合理统计功效。该方法已集成于配套的rapsem R包中，并通过教育研究领域的实证案例展示了其实用价值。代码详见https://github.com/PsychometricsMZ/RAPSEM。