This article delves deeper into the causal perspective of Necessary Condition Analysis (NCA). In contrast to traditional probabilistic sufficiency approaches in quantitative social science research about what will happen on average in a group of cases, NCA is interested in what will not occur in almost every case if a necessary condition is absent. Rooted in David Hume's theory of causation NCA explores factors that act as ‘must-haves’ or bottlenecks for the outcome. Operating from this necessity perspective, NCA functions deterministically (without exceptions) or non-deterministically (allowing exceptions). NCA can enrich theories and models in social science research by identifying essential factors. The article contributes to the literature by precisely describing the necessity causal perspective that is used in NCA, and by explaining how this is different from causal perspectives that are commonly used in social science research.
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