Methoden vergelijken

	Bayesiaanse Instrumentele Variabelen (Bayesian IV)	Bayesian Regressie
Vakgebied	Causale inferentie	Bayesiaanse statistiek
Familie	Regression model	Bayesian methods
Jaar van ontstaan	2003	—
Grondlegger	Kleibergen & Zivot (2003); Lancaster (2004)	—
Type	Causal inference / Bayesian estimation	Bayesian linear model
Oorspronkelijke bron	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
Aliassen	Bayesian IV, Bayesian 2SLS, Bayesian LIML, BayesIV	bayesian linear regression, probabilistic regression, bayesian regresyon
Verwant	6	2
Samenvatting	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.