Vertaile menetelmiä

	Bayesiläinen regressioepäjatkuvuussuunnitelma	Paikallinen keskimääräinen vaikutus (LATE / CACE)
Tieteenala	Kausaalipäättely	Kausaalipäättely
Menetelmäperhe	Regression model	Regression model
Syntyvuosi	2004-2016	1994
Kehittäjä	Karabatsos & Walker; Chib & Jacobi	Imbens & Angrist (1994); Angrist, Imbens & Rubin (1996)
Tyyppi	Bayesian causal inference / quasi-experimental	Instrumental-variable causal estimand
Alkuperäislähde	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.	Imbens, G. W., & Angrist, J. D. (1994). Identification and Estimation of Local Average Treatment Effects. Econometrica, 62(2), 467-475.
Rinnakkaisnimet	Bayesian RDD, Bayesian RD, Bayes RDD, Bayesian regression-discontinuity	LATE, CACE, complier average causal effect, Yerel Ortalama Tedavi Etkisi (LATE / CACE)
Liittyvät	5	5
Tiivistelmä	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.	The Local Average Treatment Effect is an instrumental-variable estimand, introduced by Imbens and Angrist (1994) and formalised with Rubin (1996), that recovers the average treatment effect for the subpopulation of compliers — units whose treatment status is actually moved by the instrument. It is closely tied to compliance analysis.