In this study, the effect of considering bounds on causal parameters of prospective ground motions (e.g., magnitude, source-to-site distance, and site condition) for the purpose of ground-motion selection is investigated. Although using bounds on causal parameters is common practice in conventional approaches for ground motion selection, there is presently no consistent approach for setting these bounds as a function of the seismic hazard at the site. A rigorous basis is developed and sensitivity analyses performed for the consideration of bounds on magnitude, source-to-site distance, and site condition for use in ground motion selection. In order to empirically illustrate the effects of various causal parameter bounds on the characteristics of selected ground motions, 78 and 36 cases of scenario seismic hazard analysis (scenario SHA) and probabilistic seismic hazard analysis (PSHA) are considered, which cover a wide range of causal parameters and site conditions. Ground motions are selected based on the generalized conditional intensity measure (GCIM) approach, which considers multiple ground motion intensity measures (IMs) and their variability in order to appropriately represent characteristics of the seismic hazard at the site. It is demonstrated that the application of relatively ‘wide’ bounds on causal parameters effectively removes ground motions with drastically different characteristics with respect to the target seismic hazard (improving computational efficiency in the selection process by reducing the subset of prospective records), and results in an improved representation of the target causal parameters. In contrast, the use of excessively ‘narrow’ bounds can lead to ground motion ensembles with a poor representation of the target IM distributions, especially for ground motions selected to represent PSHA results. As a result, the causal parameter bound criteria advocated in this study provide a good ‘default’ that is expected to be sufficient in the majority of problems encountered in seismic hazard and demand analyses.