Võrdle meetodeid

	Tsentraalsuse analüüs	Instrumentaalmuutujate (IV) meetod kausaalse järelduse tegemiseks	Ruumi viivis mudel (SAR / ruumiline autoregressiivne)
Valdkond	Võrgustikuanalüüs	Terviseökonoomika	Ruumianalüüs
Perekond	Process / pipeline	Process / pipeline	Regression model
Tekkeaasta	1979	1990s (modern applications)	1988
Looja	Linton C. Freeman	Angrist & Pischke (applied econometrics); rooted in econometric theory	Anselin (textbook formalisation); LeSage & Pace
Tüüp	Descriptive / exploratory network measure family	Method	Spatial autoregressive regression
Algallikas	Freeman, L.C. (1979). Centrality in Social Networks: Conceptual Clarification. Social Networks, 1(3), 215-239.	Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.	Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic.
Rööpnimetused	Merkeziyet Analizi (Degree, Betweenness, Eigenvector), node centrality, centrality measures, graph centrality	IV, two-stage least squares, TSLS, causal estimation	SAR model, spatial autoregressive model, spatial lag, Uzamsal Gecikme Modeli (SAR / Spatial Lag)
Seotud	5	3	5
Kokkuvõte	Centrality 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.	The Spatial Lag Model is an autoregressive regression that assumes spatial dependence in the dependent variable itself: the outcome values of neighbouring units enter the model as an explanatory term (ρWy). It was formalised in Anselin's Spatial Econometrics (1988) and developed further by LeSage and Pace (2009), and it decomposes spillover effects into direct, indirect, and total impacts.