Exploring the causal robustness of cross-lagged effects in Random Intercept Cross-Lagged Panel Models under conditions of omitted covariate processes

The project will investigate the causal robustness of estimates of cross-lagged effects in the RI-CLPM in response to omission of confounding processes (i.e., protection of parameter estimates against omitted-variable bias). To this end, inputs to analytic bias formulas derived by Robitzsch & Lüdtke (2025) and Guo et al. (2025) for (covariance-)stationary RI-CLPMs (Figure 1; see Section Data-Generating Mechanism) and CLPMs will be systematically varied, rendering this design a deterministic numerical evaluation. Biases in cross-lagged effects of the CLPM and RI-CLPM will be compared in order to investigate compensation properties of the RI-CLPM in response to omission of confounding processes described in Guo et al. (2025).