These authors agree with Bainter and Bollen that causal effects represents a useful measurement structure in some applications. The structure of the science of the measurement problem should determine the model; the measurement model should not determine the science. They also applaud Bainter and Bollen's important reminder that the full measurement model needs to be fully tested even if each individual component shows adequate fit, an admonition that also applies to approaches that validate traditional measurement models using other constructs (e.g., Lengua, West, & Sandler, 2008). At the same time, they believe that issues of test construction associated with defining, refining, and validating causal effect constructs have not been fully addressed; and they worry that the theoretical advantages of Bainter and Bollen's causal effects model may not be worth the confusion and biased estimates that can occur under misspecification. These authors speculate that the use of the weighted composite model to represent the causal effects structure may be less sensitive to misspecification and serve as a better measurement model in practice.