Temporality-driven covariate classification had limited impact on: the specification of directed acyclic graphs (DAGs) by 85 novice analysts (medical undergraduates); or the risk of bias in DAG-informed multivariable models designed to generate causal inference from observational data. Only 71 students (83.5%) managed to complete the "Temporality-driven Covariate Classification" task, and fewer still completed the "DAG Specification" task (77.6%) or both tasks in succession (68.2%). Most students who completed the first task misclassified at least one covariate (84.5%), and misclassification rates were even higher among students who specified a DAG (92.4%). Nonetheless, across the 512 and 517 covariates considered by each of these tasks, "confounders" were far less likely to be misclassified (11/252, 4.4% and 8/261, 3.1%) than "mediators" (70/123, 56.9% and 56/115, 48.7%) or "competing exposures" (93/137, 67.9% and 86/138, 62.3%), respectively. Since estimates of total causal effects are biased in multivariable models that: fail to adjust for "confounders"; or adjust for "mediators" (or "consequences of the outcome") misclassified as "confounders" or "competing exposures," a substantial proportion of any models informed by the present study's DAGs would have generated biased estimates of total causal effects (50/66, 76.8%); and this would have only been slightly lower for models informed by temporality-driven covariate classification alone (47/71, 66.2%). Supplementary materials for this article are available online.