Identifying Unmeasured Confounders in Panel Causal Models: A Two-Stage LM-Wald Approach
面板因果模型中未测混杂的识别诊断:两阶段 LM–Wald 方法
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…