The latent factor in a causal indicator model is no more than the latent factor of the factor part of the model. However, if the causal indicator variables are well-understood and help to improve the prediction of individuals' factor scores, they can help to interpret the meaning of the latent factor. Aguirre-Urreta, Rönkkö, and Marakas (2016) have provided an important theoretical discussion as well as empirical demonstration that in so-called causal indicator models, the meaning or interpretation of the latent variable should be based on the variables that define the common factor and not on the variables that predict the common factor. Figure 2 of Aguirre-Urreta et al., a mimic model, illustrates the point well. In this model, exogenous variables x[subscript 1] and x[subscript 2] imperfectly predict a latent factor ? that in turn predicts endogenous variables y[subscript 1] and y[subscript 2], and the issue at hand is whether the meaning of ? is derived from the x or the y variables. They conclude that although the x variables may help to identify the model, the latent factor ? must be interpreted via the endogenous y[subscript 1] and y[subscript 2] variables and not via the exogenous x[subscript 1] and x[subscript 2] variables. In this article, the author argues that such a model can help improve one's understanding of what a latent variable might actually be; that is, it may shed light on the meaning or interpretation of a latent variable.