Comparer des méthodes

	Méthode des variables instrumentales (VI) pour l'inférence causale	Régression logistique
Domaine	Économie de la santé	Statistiques de recherche
Famille	Process / pipeline	Process / pipeline
Année d'origine	1990s (modern applications)	1958
Auteur d'origine	Angrist & Pischke (applied econometrics); rooted in econometric theory	David Roxbee Cox
Type	Method	Method
Source fondatrice	Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
Alias	IV, two-stage least squares, TSLS, causal estimation	logit model, binomial logistic regression, LR
Apparentées	3	3
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.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.