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Many researchers have identified distribution shift as a likely contributorto the reproducibility crisis in behavioral and biomedical sciences. The ideais that if treatment effects vary across individual characteristics andexperimental contexts, then studies conducted in different populations willestimate different average effects. This paper uses ``generalizability" methodsto quantify how much of the effect size discrepancy between an original studyand its replication can be explained by distribution shift on observedunit-level characteristics. More specifically, we decompose this discrepancyinto ``components" attributable to sampling variability (including publicationbias), observable distribution shifts, and residual factors. We compute thisdecomposition for several directly-replicated behavioral science experimentsand find little evidence that observable distribution shifts contributeappreciably to non-replicability. In some cases, this is because there is toomuch statistical noise. In other cases, there is strong evidence thatcontrolling for additional moderators is necessary for reliable replication.

Diagnosing the role of observable distribution shift in scientific replications