One of the major problems in categorization research is the lack of systematic ways of constraining feature weights. We propose one method of operationalizing feature centrality, a causal status hypothesis which states that a cause feature is judged to be more central than its effect feature in categorization. In Experiment 1, participants learned a novel category with three characteristic features that were causally related into a single causal chain and judged the likelihood that new objects belong to the category. Likelihood ratings for items missing the most fundamental cause were lower than those for items missing the intermediate cause, which in turn were lower than those for items missing the terminal effect. The causal status effect was also obtained in goodness-of-exemplar judgments (Experiment 2) and in free-sorting tasks (Experiment 3), but it was weaker in similarity judgments than in categorization judgments (Experiment 4). Experiment 5 shows that the size of the causal status effect is moderated by plausibility of causal relations, and Experiment 6 shows that effect features can be useful in retrieving information about unknown causes. We discuss the scope of the causal status effect and its implications for categorization research.