Recent neuroimaging studies demonstrated that task-evoked functional connectivity (FC) may better predict individual traits than resting-state FC. However, the prediction properties of task-evoked effective connectivity (EC) remain unexplored. We investigated this by predicting individual reaction time (RT) performance in the stimulus-response compatibility task and age, using intrinsic EC (I-EC; calculated at baseline) and task-modulated EC (M-EC; induced by experimental conditions) with dynamic causal modeling (DCM) across various data processing conditions, including different general linear model (GLM) designs, Bayesian model reduction, and different cross-validation schemes and prediction models. We report evident differences in predicting RT and age between I-EC and M-EC, as well as between event-related and block-based GLM and DCM designs. M-EC outperformed both I-EC and task-evoked FC in RT prediction, while all types of connectivity performed similarly for age. Event-related GLM and DCM designs performed better than block-based designs. Our findings suggest that task-evoked I-EC and M-EC may capture different phenotypic attributes, with performance influenced by data processing and modeling choices, particularly the GLM-DCM design. This evaluation of methods for behavior prediction from brain EC may contribute to a meta-scientific understanding of how data processing and modeling frameworks influence neuroimaging-based predictions, offering insights for improving their robustness and efficacy. Author Summary We investigated how brain task-evoked effective connectivity (EC) can predict individual differences in behavior and age. We examined two types of EC: intrinsic EC (calculated at baseline) and task-modulated EC (induced by experimental conditions) calculated by dynamic causal modeling across various data processing conditions. We found that the task-modulated EC outperformed intrinsic EC in predicting reaction time measured during a stimulus-response task, while both EC types performed similarly in age prediction. Our findings may suggest that different EC types could capture distinct phenotypic traits, with performance influenced by data processing and modeling choices. This evaluation may further promote the application of model-based approaches to behavior prediction from brain connectivity and enhance our understanding of the impact of data processing on the prediction results.