The authors of the focus article describe an important issue related to the use and interpretation of causal indicators within the context of structural equation modeling (SEM). In the focus article, the authors illustrate with simulated data the effects of omitting a causal indicator. Since SEMs are used extensively in the social and behavioral sciences to define key constructs, it is important to examine the use of casual indicators within the context of structural equations with latent variables. One of the controversies in using SEMs is related to the use of various types of indicators for defining latent variables to represent constructs. There are several alternative measurement paradigms (Engelhard & Wang, 2014) including SEM and item response theory (IRT). The essence of the distinctions between the models can be viewed in terms of the theoretically implied directions of causality. Causal indicators represent the influence of observed variables on a latent variable, while effect indicators represent the latent variable as affecting observed indicators (Wang, Engelhard, & Lu, 2014). An IRT model can be viewed as an effect indicator measurement model from an SEM perspective with other special features, such as invariant measurement (Engelhard, 2013). There is vigorous debate among researchers regarding the appropriate use of causal indicators in a measurement model. Although some researchers support the use of causal indicators in a measurement model (Diamantopoulos & Winklhofer, 2001; Jarvis, MacKenzie, & Podsakoff, 2003), we are persuaded by other methodologists who have urged caution in using causal indicator models (Edwards, 2011; Hardin & Marcoulides, 2011; Howell, Breivik, & Wilcox, 2013). In our commentary, we briefly reflect on some of the major conclusions from the focus article.