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Is it reasonable for humans to draw temporal conclusions from omissive causal assertions? For example, if you learn that not charging your phone caused it to die, is it sensible to infer that your failure to charge your phone occurred before it died? The conclusion seems intuitive, but no theory of causal reasoning explains how people make the inference other than a recent proposal by Khemlani and colleagues [2018a]. Other theories either treat omissions as non-events, i.e., they have no location in space or time; or they account for omissions as entities that have no explicit temporal component. Theories of omissions as non-events predict that people might refrain from drawing conclusions when asked whether an omissive cause precedes its effect; theories with- out any temporal component make no prediction. We thus present Khemlani and colleagues’ [2018a] theory and describe two experiments that tested its pre- dictions. The results of the experiments speak in favor of a view that omissive causation imposes temporal constraints on events and their effects; these findings speak against predictions of the non-event view. We conclude by consider- ing whether drawing a temporal conclusion from an omissive cause constitutes a reasoning error and discuss implications for AI systems designed to compute causal inferences.