Controlling for confounding factors is one of the central aspects of quantitative research. While methods like linear regression models are common, their results can be misleading under certain conditions. We demonstrate how statistical matching can be utilized as an alternative that enables the inspection of post-matching balancing. This contribution serves as an empirical demonstration of matching in bibliometrics and discusses the advantages and potential pitfalls. We propose matching as an easy-to-use approach in bibliometrics to estimate effects and remove bias. To exemplify matching, we use data about papers published in Physical Review E and a selection classified as milestone papers. We analyze whether milestone papers score higher in terms of a proposed class of indicators for measuring disruptiveness than nonmilestone papers. We consider disruption indicators DI1, DI5, DI1n, DI5n, and DEP and test which of the disruption indicators performs best, based on the assumption that milestone papers should have higher disruption indicator values than nonmilestone papers. Four matching algorithms (propensity score matching (PSM), coarsened exact matching (CEM), entropy balancing (EB), and inverse probability weighting (IPTW)) are compared. We find that CEM and EB perform best regarding covariate balancing and DI5 and DEP performing well to evaluate disruptiveness of published papers.

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Handling Editor: Ludo Waltman

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