Salīdzināt metodes

	Beijesiskā regresijas pārtraukuma dizains	Fuzzy Regression Discontinuity Design
Nozare	Cēloņsakarību secināšana	Cēloņsakarību secināšana
Saime	Regression model	Regression model
Izcelsmes gads	2004-2016	2001
Autors	Karabatsos & Walker; Chib & Jacobi	Hahn, Todd & van der Klaauw
Tips	Bayesian causal inference / quasi-experimental	Quasi-experimental causal inference
Pirmavots	Karabatsos, G., & Walker, S. G. (2004). Coherent inference in regression discontinuity designs with a Bayesian nonparametric approach. Journal of the American Statistical Association, 99(468), 1121-1131.	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.
Citi nosaukumi	Bayesian RDD, Bayesian RD, Bayes RDD, Bayesian regression-discontinuity	Fuzzy RD, Fuzzy RDD, Fuzzy RD Design, Imperfect RDD
Saistītās	5	5
Kopsavilkums	Bayesian Regression Discontinuity Design (Bayesian RDD) embeds the classical RD framework — which estimates a local causal effect at a known assignment cutoff — within a Bayesian inferential engine. Prior distributions are placed on the regression functions on either side of the cutoff and on the treatment-effect parameter, yielding a full posterior distribution over the causal estimand rather than a single point estimate with a frequentist p-value.	Fuzzy Regression Discontinuity Design (Fuzzy RDD) estimates causal effects when eligibility for a treatment is determined by a threshold on a running variable but actual take-up of that treatment is imperfect — some eligible units do not receive treatment and some ineligible units do. The cutoff acts as an instrument, and the estimand is a Local Average Treatment Effect (LATE) for compliers near the threshold.