方法对比

	贝叶斯工具变量 (Bayesian IV)	Bayesian Regression
领域	因果推断	贝叶斯
方法族	Regression model	Bayesian methods
起源年份	2003	—
提出者	Kleibergen & Zivot (2003); Lancaster (2004)	—
类型	Causal inference / Bayesian estimation	Bayesian linear model
开创性文献	Kleibergen, F., & Zivot, E. (2003). Bayesian and classical approaches to instrumental variable regression. Journal of Econometrics, 114(1), 29-72.	Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955
别名	Bayesian IV, Bayesian 2SLS, Bayesian LIML, BayesIV	bayesian linear regression, probabilistic regression, bayesian regresyon
相关	6	2
摘要	Bayesian Instrumental Variables combines the instrumental variable strategy for addressing endogeneity with Bayesian posterior inference. Instead of relying on asymptotic sampling distributions, it places prior distributions over all structural parameters and recovers a full posterior distribution for the causal effect, providing probability statements about the parameter rather than p-values — especially valuable when instruments are weak or the sample is small.	Bayesian regression is a probabilistic version of linear regression that treats the model parameters as uncertain quantities. Instead of returning a single best-fit estimate, it combines prior knowledge with the observed data to produce a full posterior probability distribution for each parameter, from which credible intervals and predictions are read off.