পদ্ধতির তুলনা করুন

	শিক্ষা গবেষণায় কার্যকারণ সম্পর্কের সংবেদনশীলতা বিশ্লেষণ	কারণগত অনুমানের জন্য ইনস্ট্রুমেন্টাল ভ্যারিয়েবলস (IV) পদ্ধতি
ক্ষেত্র	কার্যকারণ অনুমান	স্বাস্থ্য অর্থনীতি
পরিবার	Regression model	Process / pipeline
উদ্ভবের বছর	1983–2002	1990s (modern applications)
প্রবর্তক	Paul R. Rosenbaum (formal framework); applied in education research by Briggs and others	Angrist & Pischke (applied econometrics); rooted in econometric theory
ধরন	Causal robustness / bias assessment	Method
মৌলিক উৎস	Rosenbaum, P. R. (2002). Observational Studies (2nd ed.). Springer. ISBN: 978-0387989679	Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.
অপর নাম	Rosenbaum sensitivity analysis, hidden-bias sensitivity analysis, causal sensitivity analysis, SA for causal education studies	IV, two-stage least squares, TSLS, causal estimation
সম্পর্কিত	6	3
সারসংক্ষেপ	Sensitivity analysis for causality in education research tests how robust a quasi-experimental finding is to unmeasured confounding. Rather than assuming all bias has been removed, it quantifies how large a hidden bias would need to be to overturn a causal conclusion — a critical safeguard when randomisation is impossible, which is common in educational settings.	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.