Comparer des méthodes

	Modèle de régression probit	Méthode des variables instrumentales (VI) pour l'inférence causale	Régression logistique	Modèle à effets fixes pour données de panel
Domaine	Économétrie	Économie de la santé	Statistiques de recherche	Économétrie
Famille	Regression model	Process / pipeline	Process / pipeline	Regression model
Année d'origine	2018	1990s (modern applications)	1958	2014
Auteur d'origine	Greene (textbook treatment); classical discrete-choice modelling	Angrist & Pischke (applied econometrics); rooted in econometric theory	David Roxbee Cox	Hsiao (textbook treatment); within transformation of panel data
Type	Binary discrete-choice model	Method	Method	Panel data regression
Source fondatrice	Greene, W. H. (2018). Econometric Analysis (8th ed.). Pearson. ISBN: 978-0134461366	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.	Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press.
Alias	probit regression, normit model, Probit Modeli	IV, two-stage least squares, TSLS, causal estimation	logit model, binomial logistic regression, LR	fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli
Apparentées	5	3	3	5
Résumé	The probit model is a regression method for a binary (0/1) outcome that maps a linear index of the predictors through the standard normal cumulative distribution function to produce a probability. It is a classical discrete-choice alternative to logistic regression, developed in standard econometrics treatments such as Greene's Econometric Analysis (2018).	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.	The Panel Data Fixed Effects model estimates relationships from panel data (the same units observed over several time periods) while controlling for unit- and/or time-specific effects, supporting causal inference. It is developed as the within estimator in standard treatments such as Hsiao's Analysis of Panel Data (2014).