পদ্ধতির তুলনা করুন

	নেটওয়ার্ক অর্থনীতি (Peer Effects)	কেন্দ্রিকতা বিশ্লেষণ	কারণগত অনুমানের জন্য ইনস্ট্রুমেন্টাল ভ্যারিয়েবলস (IV) পদ্ধতি	Spatial Lag Model (SAR / Spatial Autoregressive)
ক্ষেত্র	অর্থমিতি	নেটওয়ার্ক বিশ্লেষণ	স্বাস্থ্য অর্থনীতি	স্থানিক বিশ্লেষণ
পরিবার	Regression model	Process / pipeline	Process / pipeline	Regression model
উদ্ভবের বছর	2009	1979	1990s (modern applications)	1988
প্রবর্তক	Yann Bramoullé, Habiba Djebbari & Bernard Fortin	Linton C. Freeman	Angrist & Pischke (applied econometrics); rooted in econometric theory	Anselin (textbook formalisation); LeSage & Pace
ধরন	Linear-in-means peer effects regression	Descriptive / exploratory network measure family	Method	Spatial autoregressive regression
মৌলিক উৎস	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.	Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic.
অপর নাম	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	SAR model, spatial autoregressive model, spatial lag, Uzamsal Gecikme Modeli (SAR / Spatial Lag)
সম্পর্কিত	3	5	3	5
সারসংক্ষেপ	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.	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.