เปรียบเทียบวิธี

	การออกแบบการถดถอยแบบช่วงคะแนน (Regression Discontinuity Design - RDD)	วิธีการตัวแปรเครื่องมือ (IV) สำหรับการอนุมานเชิงสาเหตุ
สาขาวิชา	เศรษฐมิติ	เศรษฐศาสตร์สุขภาพ
ตระกูล	Regression model	Process / pipeline
ปีกำเนิด	2008	1990s (modern applications)
ผู้ริเริ่ม	Imbens & Lemieux; Lee & Lemieux (modern practice); Cattaneo, Idrobo & Titiunik	Angrist & Pischke (applied econometrics); rooted in econometric theory
ประเภท	Quasi-experimental causal design	Method
แหล่งต้นตำรับ	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: Princeton University Press.
ชื่อเรียกอื่น	RDD, regression discontinuity, sharp regression discontinuity, Regresyon Süreksizliği Tasarımı (RDD)	IV, two-stage least squares, TSLS, causal estimation
ที่เกี่ยวข้อง	5	3
สรุป	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).	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.