Explaining why events occurred involves solving different information-processing problems: inferring what actually happened (causal inference) but also highlighting a subset of the causes that contributed to the outcome (causal selection). While past research has investigated causal inference and causal selection separately, we report results of an experiment (N=284) examining how people solve both problems jointly, as is the case in real-world explanation settings. We find evidence that participants infer the state of unobserved variables on the basis of available evidence, and observe common behavioral signatures of causal selection. However, explanation preferences deviate in important ways from the predictions of a computational model combining existing theories of causal inference and causal selection. In particular, participants were disproportionately likely to select inferred over observed variables. We suggest a possible preference for producing explanations that allow the explainee to benefit from inferential work performed by the explainer.