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Sentralitetsanalyse×Instrumentelle variabler (IV) metode for kausal inferens×
FagfeltNettverksanalyseHelseøkonomi
FamilieProcess / pipelineProcess / pipeline
Opprinnelsesår19791990s (modern applications)
OpphavspersonLinton C. FreemanAngrist & Pischke (applied econometrics); rooted in econometric theory
TypeDescriptive / exploratory network measure familyMethod
Opprinnelig kildeFreeman, L.C. (1979). Centrality in Social Networks: Conceptual Clarification. Social Networks, 1(3), 215-239. DOI ↗Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press. link ↗
AliasMerkeziyet Analizi (Degree, Betweenness, Eigenvector), node centrality, centrality measures, graph centralityIV, two-stage least squares, TSLS, causal estimation
Relaterte53
SammendragCentrality analysis is a family of network-analytic measures, formalized by Freeman (1979), that quantifies the structural importance of individual nodes within a graph. Each centrality index captures a distinct mechanism of influence: degree centrality reflects direct connectivity, betweenness centrality identifies nodes that broker information flow, closeness centrality captures proximity to all others, and eigenvector centrality (along with PageRank) rewards connection to highly connected neighbors.Instrumental variables (IV) is an econometric method to estimate causal effects when treatment or exposure is not randomly assigned and confounding is severe or unmeasured. IV relies on a third variable (instrument) that influences treatment but does not directly affect the outcome, allowing researchers to isolate the causal effect from the noise of confounding. Developed extensively in econometrics (Angrist & Pischke, 1990s–2000s), IV methods are increasingly used in health economics and health services research to leverage natural experiments and policy changes.
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ScholarGateSammenlign metoder: Centrality Analysis · Instrumental Variables in Health Research. Hentet 2026-06-19 fra https://scholargate.app/no/compare