So sánh phương pháp

	Thiết kế Gián đoạn Hồi quy Mờ Đa kỳ	Phương pháp Biến Công cụ (IV) cho Suy luận Nhân quả
Lĩnh vực	Suy luận nhân quả	Kinh tế học y tế
Họ	Regression model	Process / pipeline
Năm ra đời	2001 (fuzzy RD); multi-period extension ~2010s	1990s (modern applications)
Người khởi xướng	Hahn, Todd & Van der Klaauw (foundational fuzzy RD, 2001); extended to multi-period settings by Cattaneo, Idrobo & Titiunik and subsequent applied literature	Angrist & Pischke (applied econometrics); rooted in econometric theory
Loại	Quasi-experimental causal inference	Method
Công trình gốc	Hahn, J., Todd, P., & Van der Klaauw, W. (2001). Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design. Review of Economic Studies, 68(1), 201-209.	Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.
Tên gọi khác	multi-period fuzzy RDD, fuzzy RD with repeated assignment, multi-wave fuzzy RD, staggered fuzzy RDD	IV, two-stage least squares, TSLS, causal estimation
Liên quan	4	3
Tóm tắt	Multi-period fuzzy regression discontinuity design estimates a local average treatment effect when a cutoff rule only partially determines treatment — that is, crossing the threshold raises the probability of treatment but does not guarantee it — and when this assignment process is observed across two or more time periods or cohorts, enabling pooled or period-specific causal estimates under repeated near-threshold comparisons.	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.