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	Dynamic Fuzzy Regression Discontinuity Design	Metoda zmiennych instrumentalnych (IV) do wnioskowania przyczynowego
Dziedzina	Wnioskowanie przyczynowe	Ekonomika zdrowia
Rodzina	Regression model	Process / pipeline
Rok powstania	2001-2010	1990s (modern applications)
Twórca	Cellini, Ferreira & Rothstein (dynamic RDD, 2010); Hahn, Todd & Van der Klaauw (fuzzy RDD foundations, 2001)	Angrist & Pischke (applied econometrics); rooted in econometric theory
Typ	Quasi-experimental causal inference	Method
Źródło pierwotne	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.
Inne nazwy	Dynamic Fuzzy RDD, DFRD, Time-varying Fuzzy RD, Dynamic Fuzzy RD Design	IV, two-stage least squares, TSLS, causal estimation
Pokrewne	4	3
Podsumowanie	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.