विधियों की तुलना करें

	Robust Regression Discontinuity Design (मजबूत रिग्रेशन डिसकंटीन्यूइटी डिज़ाइन)	कारण अनुमान के लिए वाद्य चर (IV) विधि
क्षेत्र	कारणात्मक अनुमान	स्वास्थ्य अर्थशास्त्र
परिवार	Regression model	Process / pipeline
उद्भव वर्ष	2014	1990s (modern applications)
प्रवर्तक	Calonico, Cattaneo & Titiunik	Angrist & Pischke (applied econometrics); rooted in econometric theory
प्रकार	Quasi-experimental causal inference	Method
मौलिक स्रोत	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.
उपनाम	Robust RDD, Bias-corrected RDD, CCT estimator, rdrobust	IV, two-stage least squares, TSLS, causal estimation
संबंधित	4	3
सारांश	Robust RDD extends the classical regression discontinuity design with bias correction and robust confidence intervals, addressing the under-coverage problem of conventional RDD inference. Developed by Calonico, Cattaneo, and Titiunik (2014), it uses local polynomial estimation with a bias-corrected point estimate and a wider variance term that accounts for the added uncertainty, yielding confidence intervals with correct asymptotic coverage.	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.