Lagged Explanatory Variables and the Estimation of Causal Effect

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The Real Exchange Rate and Economic Growth: Revisiting the Case Using External Instruments (2017, background): “Notably, Reed (2015) and Bellemare et al. (2015) show that lag identification depends on the assumption that the unobserved confounding variable is not serially correlated but the lagged endogenous variable is, which is unlikely.”
How to Deal With Reverse Causality Using Panel Data? Recommendations for Researchers Based on a Simulation Study (2019, background): “Therefore, simply lagging X in RE or in FE models rarely solves the endogeneity problem posed by unobserved variables (see Bellemare et al. 2017:960).”
Status threat, not economic hardship, explains the 2016 presidential vote (2018, neutral): “The motivation for using 2011 attitudes is said to be eliminating concerns regarding endogeneity: that is, “the possibility that people changed their attitudes to match their candidate preference” (3); however, methodologists concur that “lag identification is almost never a solution to endogeneity ”