Vertaile menetelmiä

	Bayesiläinen sumea regressioepäjatkuvuus	Bayesiläiset instrumenttimuuttujat (Bayesian IV)
Tieteenala	Kausaalipäättely	Kausaalipäättely
Menetelmäperhe	Regression model	Regression model
Syntyvuosi	2001 (fuzzy RD identification); 2016 (Bayesian formulation by Chib & Jacobi)	2003
Kehittäjä	Chib & Jacobi (Bayesian formulation); Hahn, Todd & Van der Klaauw (fuzzy RD identification)	Kleibergen & Zivot (2003); Lancaster (2004)
Tyyppi	Bayesian causal inference / quasi-experimental design	Causal inference / Bayesian estimation
Alkuperäislähde	Hahn, J., Todd, P., & Van der Klaauw, W. (2001). Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design. Review of Economic Studies, 68(1), 201-209.	Kleibergen, F., & Zivot, E. (2003). Bayesian and classical approaches to instrumental variable regression. Journal of Econometrics, 114(1), 29-72.
Rinnakkaisnimet	Bayesian Fuzzy RD, Bayesian Fuzzy RDD, Fuzzy RD with Bayesian Inference	Bayesian IV, Bayesian 2SLS, Bayesian LIML, BayesIV
Liittyvät	5	6
Tiivistelmä	Bayesian Fuzzy Regression Discontinuity (Bayesian Fuzzy RD) combines the quasi-experimental logic of fuzzy regression discontinuity design with full Bayesian inference. It estimates a local average treatment effect at a policy threshold where treatment assignment is probabilistic rather than deterministic, placing prior distributions over all unknowns and recovering a complete posterior distribution of the causal effect rather than a single point estimate.	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.