方法对比

	回归断点设计 (Regression Discontinuity Design, RDD)	双重差分法 (Diff-in-Diff)	因果推断的工具变量（IV）方法
领域	计量经济学	计量经济学	卫生经济学
方法族	Regression model	Regression model	Process / pipeline
起源年份	2008	1994	1990s (modern applications)
提出者	Imbens & Lemieux; Lee & Lemieux (modern practice); Cattaneo, Idrobo & Titiunik	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)	Angrist & Pischke (applied econometrics); rooted in econometric theory
类型	Quasi-experimental causal design	Causal inference / panel regression	Method
开创性文献	Imbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635.	Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355	Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.
别名	RDD, regression discontinuity, sharp regression discontinuity, Regresyon Süreksizliği Tasarımı (RDD)	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)	IV, two-stage least squares, TSLS, causal estimation
相关	5	5	3
摘要	Regression Discontinuity Design is a quasi-experimental method that estimates a local causal effect around a threshold (cutoff) value, comparing units just below and just above the cutoff as if they were almost randomly assigned. It is the design developed for applied practice by Imbens and Lemieux (2008) and by Lee and Lemieux (2010).	Difference-in-Differences is a causal-inference method that estimates the effect of an intervention by comparing how a treatment group and a control group change over time. Made famous by Card and Krueger's 1994 minimum-wage study and developed in Angrist and Pischke's Mostly Harmless Econometrics, it isolates the treatment effect as the difference between the two groups' before-after changes.	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.