The use of causal indicators to formatively measure latent constructs appears to be on the rise, despite what appears to be a troubling lack of consistency in their application. Scholars in any discipline are responsible not only for advancing theoretical knowledge in their domain of study but also for addressing methodological issues that threaten that advance. In that spirit, the current study traces causal indicators from their origins in causal modeling to their use in structural equation modeling today. Conclusions from this review suggest that unlike effect (reflective) indicators, whose application is based on classical test theory, today's application of causal (formative) indicators is based on research demonstrating their practical application rather than on psychometric theory supporting their use. The authors suggest that this lack of theory has contributed to the confusion surrounding their implementation. Recent research has questioned the generalizability of formatively measured latent constructs. In the current study, the authors discuss how the use of fixed-weight composites may be one way to employ causal indicators so that they may be generalized to additional contexts. More specifically, they suggest the use of meta-analysis principles for identifying optimum causal indicator weights that can be used to generate fixed-weight composites. Finally, the authors explain how these fixed-weight composites can be implemented in both components-based and covariance-based statistical packages. Implications for the use of causal indicators in academic research are used to focus these discussions. (Contains 4 notes and 9 figures.)