The first half of the present chapter considers formative measurement models. Similar advantages to those found in reflective models also apply to formative models. However, formative models face two key challenges: causal misspecification and a seeming proliferation of causal composite variables. Different causal interpretations handle these challenges differently. A latter section briefly discusses a causal interpretation appropriate for network models. The last portion of the chapter focuses on causal interpretations appropriate to individual differences. Taken together, these topics canvass a number of open questions with respect to causation and assessment. Nonetheless, the chapter by no means exhausts the problems waiting to be solved. A box at the end summarizes some key open questions from the chapter.