Making models up

Abstract Theories of causation within the causal modeling framework are among the most promising and well‐developed approaches to analyzing actual causation today. But since the advent of model‐based theories of causation, authors have struggled with the fact that virtually all such theories issue different causal conclusions when applied to different causal models (of the same case). In recent years, these concerns about model relativity have sharpened into a search for a general theory of model aptness , which lays out principled grounds on which models may be ruled out as inapt for causal analysis. This article presents a new way of understanding what drives model relativity that shows why it is such an enduring problem. I show why extant proposals fail and furthermore, why they are not of the right form to be a solution. Along the way, I draw broader lessons about what an adequate answer to these problems must be like. I then take a step toward such an account by outlining a substantive constraint on when variables and paths in a causal model must not be drawn and when they may.