পদ্ধতির তুলনা করুন

	Robust Regression Discontinuity Design	কারণগত অনুমানের জন্য ইনস্ট্রুমেন্টাল ভ্যারিয়েবলস (IV) পদ্ধতি
ক্ষেত্র	কার্যকারণ অনুমান	স্বাস্থ্য অর্থনীতি
পরিবার	Regression model	Process / pipeline
উদ্ভবের বছর	2014	1990s (modern applications)
প্রবর্তক	Calonico, Cattaneo & Titiunik	Angrist & Pischke (applied econometrics); rooted in econometric theory
ধরন	Quasi-experimental causal inference	Method
মৌলিক উৎস	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.
অপর নাম	Robust RDD, Bias-corrected RDD, CCT estimator, rdrobust	IV, two-stage least squares, TSLS, causal estimation
সম্পর্কিত	4	3
সারসংক্ষেপ	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.