เปรียบเทียบวิธี

	Spatial Regression Discontinuity Design	วิธีการตัวแปรเครื่องมือ (IV) สำหรับการอนุมานเชิงสาเหตุ
สาขาวิชา	การอนุมานเชิงสาเหตุ	เศรษฐศาสตร์สุขภาพ
ตระกูล	Regression model	Process / pipeline
ปีกำเนิด	2010s	1990s (modern applications)
ผู้ริเริ่ม	Popularized by Dell (2010); formalized for geographic boundaries by Keele & Titiunik (2015)	Angrist & Pischke (applied econometrics); rooted in econometric theory
ประเภท	Quasi-experimental causal inference	Method
แหล่งต้นตำรับ	Dell, M. (2010). The Persistent Effects of Peru's Mining Mita. Econometrica, 78(6), 1863-1903.	Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.
ชื่อเรียกอื่น	Spatial RDD, Geographic RDD, Border RD Design, Geographic Discontinuity Design	IV, two-stage least squares, TSLS, causal estimation
ที่เกี่ยวข้อง	4	3
สรุป	Spatial Regression Discontinuity Design uses a geographic or administrative boundary as the threshold that assigns units to treatment. Observations just inside one side of the boundary are compared with those just outside it, exploiting the near-random variation in treatment status near the cutoff to recover a local causal effect. The approach is widely used in economics, political science, and public health when policies or institutions change sharply at a border.	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.