Panel data are widely used in political science to draw causal inferences. However, these models often rely on the strong and untested assumption of sequential ignorability--that no unmeasured variables influence both the independent and outcome variables across time. Grounded in psychometric literature on latent variable modeling, this paper introduces the Two-Stage LM-Wald (2SLW) approach, a diagnostic tool that extends the Lagrange Multiplier (LM) and Wald tests to detect violations of this assumption in panel causal models. Using Monte Carlo simulations within the Random Intercept Cross-Lagged Panel Model (RI-CLPM), which separates within and between person effects, I demonstrate the 2SLW's ability to detect unmeasured confounding across three key scenarios: biased corrections, distorted direct effects, and altered mediation pathways. I also illustrate the approach with an empirical application to real-world panel data. By providing a practical and theoretically grounded diagnostic, the 2SLW approach enhances the robustness of causal inferences in the presence of potential time-varying confounders. Moreover, it can be readily implemented using the R package lavaan.

翻译：面板数据在政治学中被广泛用于进行因果推断。然而，这些模型通常依赖于一个强且未经检验的序列可忽略性假设——即不存在未测量的变量随时间同时影响自变量和结果变量。本文基于心理测量学中关于潜变量建模的文献，提出了两阶段LM-Wald（2SLW）方法，这是一种诊断工具，它扩展了拉格朗日乘子（LM）检验和Wald检验，用于检测面板因果模型中对此假设的违背。通过在区分个体内与个体间效应的随机截距交叉滞后面板模型（RI-CLPM）中进行蒙特卡洛模拟，我展示了2SLW在三种关键情境下检测未测混杂因素的能力：有偏校正、扭曲的直接效应以及改变的中介路径。我还通过一个对真实世界面板数据的实证应用来演示该方法。通过提供一种实用且理论依据充分的诊断工具，2SLW方法增强了在存在潜在时变混杂因素情况下因果推断的稳健性。此外，该方法可以方便地使用R软件包lavaan实现。