Compara mètodes

	Econometria de xarxes (efectes de parell)	Anàlisi de Centralitat	Mètode de Variables Instrumentals (IV) per a la Inferència Causal
Camp	Econometria	Anàlisi de xarxes	Economia de la salut
Família	Regression model	Process / pipeline	Process / pipeline
Any d'origen	2009	1979	1990s (modern applications)
Autor original	Yann Bramoullé, Habiba Djebbari & Bernard Fortin	Linton C. Freeman	Angrist & Pischke (applied econometrics); rooted in econometric theory
Tipus	Linear-in-means peer effects regression	Descriptive / exploratory network measure family	Method
Font seminal	Bramoullé, Y., Djebbari, H., & Fortin, B. (2009). Identification of peer effects through social networks. Journal of Econometrics, 150(1), 41–55.	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.
Àlies	Social Interactions Model, Peer Effects Model, Social Network Regression, Ağ Ekonometrisi	Merkeziyet Analizi (Degree, Betweenness, Eigenvector), node centrality, centrality measures, graph centrality	IV, two-stage least squares, TSLS, causal estimation
Relacionats	3	5	3
Resum	Network econometrics estimates how individuals' outcomes are causally shaped by the behaviour and characteristics of their social-network neighbours. Formalised by Bramoullé, Djebbari, and Fortin (2009), the framework embeds a row-normalised adjacency matrix into a linear regression, separating endogenous peer effects (imitation of outcomes), exogenous contextual effects (influence of neighbours' attributes), and correlated effects (shared environment), while using network topology to construct valid instruments.	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.