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Description: Robust scientific knowledge is contingent upon replication of original findings. However, researchers who conduct replication studies face a difficult problem; there are many more studies in need of replication than there are funds available for replicating. To select studies for replication efficiently, we need to understand which studies are the most in need of replication. In other words, we need to understand which replication efforts have the highest expected utility. In this article we propose a general rule for study selection in replication research based on the replication value of the claims considered for replication. The replication value of a claim is defined as the maximum expected utility we could gain by replicating the claim, and is a function of (1) the value of being certain about the claim, and (2) uncertainty about the claim based on current evidence. We formalize this definition in terms of a causal decision model, utilizing concepts from decision theory and causal graph modeling. We discuss the validity of using replication value as a measure of expected utility gain, and we suggest approaches for deriving quantitative estimates of replication value.