방법 비교

	프로빗 회귀 모형	인과 추론을 위한 도구 변수(IV) 방법	로지스틱 회귀
분야	계량경제학	보건경제학	연구 통계
계열	Regression model	Process / pipeline	Process / pipeline
기원 연도	2018	1990s (modern applications)	1958
창시자	Greene (textbook treatment); classical discrete-choice modelling	Angrist & Pischke (applied econometrics); rooted in econometric theory	David Roxbee Cox
유형	Binary discrete-choice model	Method	Method
원전	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.
별칭	probit regression, normit model, Probit Modeli	IV, two-stage least squares, TSLS, causal estimation	logit model, binomial logistic regression, LR
관련	5	3	3
요약	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.