 Gain Scores Revisited: A Graphical Models PerspectiveYongnam Kim and Peter M. Steiner Sociological Methods & Research, 2021, vol. 50, issue 3, 1353-1375 Abstract: For misguided reasons, social scientists have long been reluctant to use gain scores for estimating causal effects. This article develops graphical models and graph-based arguments to show that gain score methods are a viable strategy for identifying causal treatment effects in observational studies. The proposed graphical models reveal that gain score methods rely on a bias-removing mechanism that is quite different to regular matching or covariance adjustment. While gain score methods offset noncausal associations via differencing, matching or covariance adjustment blocks noncausal association via conditioning. Since gain score estimators do not rely on conditioning, they are immune to measurement error in the pretest, bias amplification, and collider bias. The graph-based arguments also demonstrate that the key identifying assumption for gain score methods, the common trend assumption, is difficult to assess and justify when the pretest causally affects treatment assignment. Finally, we discuss the distinct role of pretests in the context of Lord’s paradox. Keywords: gain score; pretest; causal graphs; common trend assumption; Lord’s paradox (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:sae:somere:v:50:y:2021:i:3:p:1353-1375 Access Statistics for this article More articles in Sociological Methods & Research |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.