方法对比

	Probit 回归模型	因果推断的工具变量（IV）方法	面板数据固定效应模型
领域	计量经济学	卫生经济学	计量经济学
方法族	Regression model	Process / pipeline	Regression model
起源年份	2018	1990s (modern applications)	2014
提出者	Greene (textbook treatment); classical discrete-choice modelling	Angrist & Pischke (applied econometrics); rooted in econometric theory	Hsiao (textbook treatment); within transformation of panel data
类型	Binary discrete-choice model	Method	Panel data regression
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.	Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press.
别名	probit regression, normit model, Probit Modeli	IV, two-stage least squares, TSLS, causal estimation	fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli
相关	5	3	5
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.	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).