Identifying Unmeasured Confounders in Panel Causal Models: A Two-Stage LM-Wald Approach

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.