To assist non-specialists in formulating database queries, multiple frameworks that automatically infer queries from a set of examples have been proposed. While highly useful, a shortcoming of the approach is that if users can only provide a small set of examples, many inherently different queries may qualify, and only some of these actually match the user intentions. Our main observation is that if users further explain their examples, the set of qualifying queries may be significantly more focused. We develop a novel framework where users explain example tuples by choosing input tuples that are intuitively the "cause" for their examples. Their explanations are automatically "compiled" into a formal model for explanations, based on previously developed models of data provenance. Then, our novel algorithms infer conjunctive queries from the examples and their explanations. We prove the computational efficiency of the algorithms and favorable properties of inferred queries. We have further implemented our solution in a system prototype with an interface that assists users in formulating explanations in an intuitive way. Our experimental results, including a user study as well as experiments using the TPC-H benchmark, indicate the effectiveness of our solution.