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Description: All explanations are incomplete descriptions, but reasoners appear willing to assess some explanations as more complete than others. To explain this behavior, we propose a novel theory of the detection of explanatory incompleteness. The account is based on the assumption that reasoners represent explanations as mental models – discrete, iconic representations of possibilities – of causal scenarios. A complete explanation refers to a single integrated model, whereas an incomplete explanation refers to multiple models. The theory predicts that if there exists an unspecified causal relation anywhere within a causal description, then reasoners will maintain multiple explanatory models to handle the disconnection. Reasoners should treat such explanations as less complete than those without such a gap. Four experiments provided participants with causal descriptions, some of which yield one explanatory model, e.g., A causes B and B causes C, and some of which demand multiple models, e.g., A causes X and B causes C. Participants across the studies preferred one-model descriptions to multiple-model ones on tasks that implicitly and explicitly required them to assess explanatory completeness. The studies corroborate the theory, and they are the first to reveal the processes that underlie the assessment explanatory completeness. We conclude by reviewing the theory in light of extant accounts of causal reasoning.