方法对比

	Probit 回归模型	因果推断的工具变量（IV）方法
领域	计量经济学	卫生经济学
方法族	Regression model	Process / pipeline
起源年份	2018	1990s (modern applications)
提出者	Greene (textbook treatment); classical discrete-choice modelling	Angrist & Pischke (applied econometrics); rooted in econometric theory
类型	Binary discrete-choice model	Method
开创性文献	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.
别名	probit regression, normit model, Probit Modeli	IV, two-stage least squares, TSLS, causal estimation
相关	5	3
摘要	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.