So sánh phương pháp

	Thiết kế Gián đoạn Hồi quy Mờ Động	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	2001-2010	1990s (modern applications)
Người khởi xướng	Cellini, Ferreira & Rothstein (dynamic RDD, 2010); Hahn, Todd & Van der Klaauw (fuzzy RDD foundations, 2001)	Angrist & Pischke (applied econometrics); rooted in econometric theory
Loại	Quasi-experimental causal inference	Method
Công trình gốc	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.
Tên gọi khác	Dynamic Fuzzy RDD, DFRD, Time-varying Fuzzy RD, Dynamic Fuzzy RD Design	IV, two-stage least squares, TSLS, causal estimation
Liên quan	4	3
Tóm tắt	Dynamic Fuzzy Regression Discontinuity Design extends the standard fuzzy RDD to a panel or multi-period setting, allowing researchers to estimate how the causal effect of a probabilistic threshold-based treatment evolves over time. By combining an IV-based fuzzy first stage with time-indexed outcomes, it traces treatment effects across multiple post-treatment periods, not just at a single cross-sectional snapshot.	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.