Latent variable structural equation modeling has become the analytic method of choice in many domains of research in psychology and allied social sciences. One important aspect of a latent variable model concerns the relations hypothesized to hold between latent variables and their indicators. The most common specification of structural equation models includes latent variables with effect indicators, such that a given latent variable is presumed to influence each of its indicators; that is, a change in the level of the latent variable should occasion a change in each of its indicators. For example, if an individual's level of numerical facility increased across time, the expectation under the model is that each of the indicators of numerical facility would exhibit a corresponding increase. Bainter and Bollen (this issue) focused on a different category of latent variable, comprising latent variables that have causal indicators. Under this approach, the manifest variables "cause" the latent variable, so that the latent variable represents an emergent variable that arises due to the effects of its indicators. Although several of the arguments by Bainter and Bollen may be of interest, the ultimate utility of latent variables with causal indicators remains in question. In this commentary, Keith Widaman states that he agrees with Bainter and Bollen (this issue) on one central point: Certain manifest variable indicators can reasonably be considered effect indicators of their latent variables, and other manifest variables should not be specified in such a fashion. However, Widaman differs with Bainter and Bollen on what should be done with the latter class of indicators. Widaman asserts that Bainter and Bollen presented one view on the utility of causal indicator latent variables. Despite the clarity with which their position was presented, much remains to be understood before causal indicators are a solid basis for replicable scientific endeavors.