Salīdzināt metodes

	Bayesiskā fuzzy regresijas atšķirības metode	Bayesian Instrumental Variables (Bayesian IV)
Nozare	Cēloņsakarību secināšana	Cēloņsakarību secināšana
Saime	Regression model	Regression model
Izcelsmes gads	2001 (fuzzy RD identification); 2016 (Bayesian formulation by Chib & Jacobi)	2003
Autors	Chib & Jacobi (Bayesian formulation); Hahn, Todd & Van der Klaauw (fuzzy RD identification)	Kleibergen & Zivot (2003); Lancaster (2004)
Tips	Bayesian causal inference / quasi-experimental design	Causal inference / Bayesian estimation
Pirmavots	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.
Citi nosaukumi	Bayesian Fuzzy RD, Bayesian Fuzzy RDD, Fuzzy RD with Bayesian Inference	Bayesian IV, Bayesian 2SLS, Bayesian LIML, BayesIV
Saistītās	5	6
Kopsavilkums	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.