方法对比

	贝叶斯回归断点设计	因果推断的工具变量（IV）方法
领域	因果推断	卫生经济学
方法族	Regression model	Process / pipeline
起源年份	2004-2016	1990s (modern applications)
提出者	Karabatsos & Walker; Chib & Jacobi	Angrist & Pischke (applied econometrics); rooted in econometric theory
类型	Bayesian causal inference / quasi-experimental	Method
开创性文献	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.	Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.
别名	Bayesian RDD, Bayesian RD, Bayes RDD, Bayesian regression-discontinuity	IV, two-stage least squares, TSLS, causal estimation
相关	5	3
摘要	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.	Instrumental variables (IV) is an econometric method to estimate causal effects when treatment or exposure is not randomly assigned and confounding is severe or unmeasured. IV relies on a third variable (instrument) that influences treatment but does not directly affect the outcome, allowing researchers to isolate the causal effect from the noise of confounding. Developed extensively in econometrics (Angrist & Pischke, 1990s–2000s), IV methods are increasingly used in health economics and health services research to leverage natural experiments and policy changes.