Võrdle meetodeid

	Robustne hägusa regressioonkatkestuse disain	Instrumentaalmuutujate (IV) meetod kausaalse järelduse tegemiseks
Valdkond	Põhjuslik järeldamine	Terviseökonoomika
Perekond	Regression model	Process / pipeline
Tekkeaasta	2014 (robust CCT estimator); 2001 (fuzzy RDD formalization)	1990s (modern applications)
Looja	Calonico, Cattaneo, and Titiunik (robust inference framework); Hahn, Todd, and Van der Klaauw (fuzzy RDD formalization)	Angrist & Pischke (applied econometrics); rooted in econometric theory
Tüüp	Quasi-experimental causal inference with IV at threshold	Method
Algallikas	Calonico, S., Cattaneo, M. D., & Titiunik, R. (2014). Robust Nonparametric Confidence Intervals for Regression-Discontinuity Designs. Econometrica, 82(6), 2295-2326.	Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.
Rööpnimetused	Robust Fuzzy RDD, Fuzzy RD with robust inference, bias-corrected fuzzy RD, CCT fuzzy RDD	IV, two-stage least squares, TSLS, causal estimation
Seotud	5	3
Kokkuvõte	Robust Fuzzy Regression Discontinuity Design estimates a local average treatment effect (LATE) at a threshold where crossing the cutoff raises — but does not guarantee — treatment receipt. Introduced by Calonico, Cattaneo, and Titiunik (2014), the robust framework applies bias-corrected local polynomial estimation with a robust variance estimator, correcting the coverage failures of conventional bandwidth-optimal inference in both the sharp and fuzzy cases.	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.