방법 비교

	네트워크 계량경제학 (동료 효과)	인과 추론을 위한 도구 변수(IV) 방법	공간 시차 모형 (SAR / 공간 자기회귀)
분야	계량경제학	보건경제학	공간분석
계열	Regression model	Process / pipeline	Regression model
기원 연도	2009	1990s (modern applications)	1988
창시자	Yann Bramoullé, Habiba Djebbari & Bernard Fortin	Angrist & Pischke (applied econometrics); rooted in econometric theory	Anselin (textbook formalisation); LeSage & Pace
유형	Linear-in-means peer effects regression	Method	Spatial autoregressive regression
원전	Bramoullé, Y., Djebbari, H., & Fortin, B. (2009). Identification of peer effects through social networks. Journal of Econometrics, 150(1), 41–55.	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.
별칭	Social Interactions Model, Peer Effects Model, Social Network Regression, Ağ Ekonometrisi	IV, two-stage least squares, TSLS, causal estimation	SAR model, spatial autoregressive model, spatial lag, Uzamsal Gecikme Modeli (SAR / Spatial Lag)
관련	3	3	5
요약	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.	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.