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

TL;DR

This paper introduces the 2SLW diagnostic method for detecting unmeasured confounders in panel causal models, improving the robustness of causal inferences in social science research.

Contribution

It extends LM and Wald tests to identify violations of sequential ignorability in panel data, grounded in latent variable modeling and applicable via R package lavaan.

Findings

2SLW effectively detects unmeasured confounding in simulations

It identifies biases in corrections and effects in panel models

Empirical application demonstrates practical utility

Abstract

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.