Confronta i metodi

	Variabili Strumentali Bayesiane (IV Bayesiana)	Regressione Bayesiana
Campo	Inferenza causale	Bayesiano
Famiglia	Regression model	Bayesian methods
Anno di origine	2003	—
Ideatore	Kleibergen & Zivot (2003); Lancaster (2004)	—
Tipo	Causal inference / Bayesian estimation	Bayesian linear model
Fonte seminale	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
Alias	Bayesian IV, Bayesian 2SLS, Bayesian LIML, BayesIV	bayesian linear regression, probabilistic regression, bayesian regresyon
Correlati	6	2
Sintesi	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.