Challenges the typical treatment of causal effects in longitudinal data, arguing that models should be conceptualized and tested in ways that directly reflect prior assumptions as to the trait- or state-like nature of the variables. Examples demonstrate that meaningful longitudinal studies of state variables can be conducted without assuming their stability over time. (Author/RH)