Causal asymmetry is a situation where the causal factors under study are more suitable for explaining the outcome than its absence (or vice versa); they do not explain both equally well. In such a situation, presence of a cause leads to presence of the effect, but absence of the cause may not lead to absence of the effect. A conceptual discussion is followed by the empirical example of gaining a degree (or not), comparing the methods logistic regression and Qualitative Comparative Analysis (QCA). While logistic regression, being based on correlational analysis and thus assuming symmetric relationships between variables, does not lend itself automatically to detecting causal asymmetry, it can be used to bring out asymmetry nevertheless. QCA, on the other hand, uncovers asymmetry, if present, by default. The closing recommendation is for researchers to be more aware of the possibility of asymmetry existing, regardless of the particular method employed.