Causal diagrams are used in biomedical research to develop and portray conceptual models that accurately and concisely convey assumptions about putative causal relations. Specifically, causal diagrams can be used for both observational studies and clinical trials to provide a scientific basis for some aspects of multivariable model selection. This methodology also provides an explicit framework for classifying potential sources of bias and enabling the identification of confounder, collider, and mediator variables for statistical analyses. We illustrate the potential serious miscalculation of effect estimates resulting from incorrect selection of variables for multivariable modeling without regard to their type and causal ordering as demonstrated by causal diagrams. Our objective is to improve researchers’ understanding of the critical variable selection process to enhance their communication with collaborating statisticians regarding the scientific basis for intended study designs and multivariable statistical analyses. We introduce the concept of causal diagrams and their development as directed acyclic graphs to illustrate the usefulness of this methodology. We present numeric examples of effect estimate calculations and miscalculations based on analyses of the well-known Framingham Heart Study. Clinical researchers can use causal diagrams to improve their understanding of complex causation relations to determine accurate and valid multivariable models for statistical analyses. The Framingham Heart Study dataset and software codes for our analyses are provided in Appendix E1 (available online at www.annemergmed.com) to allow readers the opportunity to conduct their analyses.