We discuss the identification, estimation, and testing of structural equation models that have causal indicators. We first provide 2 rules of identification that are particularly helpful in models with causal indicators--the 2C emitted paths rule and the exogenous X rule. We demonstrate how these rules can help us distinguish identified from underidentified models. Estimation and testing of identified models with causal indicators is straightforward. For underidentified models with causal indicators we develop "partially reduced form" and "zero error variance" models that are implied by the original model and use these to estimate and test aspects of the original model. We can test the overall fit of the model, the proportionality constraints sometimes implied by a latent variable with causal indicators, and the statistical significance of parameter estimates. Two empirical examples illustrate the procedures. Our results substantiate that it is feasible to incorporate causal indicators in modeling efforts. (Contains 9 figures and 23 footnotes.)