Comparer des méthodes

	Méthode des variables instrumentales (VI) pour l'inférence causale	Régression par Moindres Carrés Ordinaires (MCO)
Domaine	Économie de la santé	Économétrie
Famille	Process / pipeline	Regression model
Année d'origine	1990s (modern applications)	2019
Auteur d'origine	Angrist & Pischke (applied econometrics); rooted in econometric theory	Wooldridge (textbook treatment); classical least squares
Type	Method	Linear regression
Source fondatrice	Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Alias	IV, two-stage least squares, TSLS, causal estimation	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Apparentées	3	5
Résumé	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.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).