विधियों की तुलना करें

	कारणता के लिए बायेसियन संवेदनशीलता विश्लेषण	कारण अनुमान के लिए वाद्य चर (IV) विधि
क्षेत्र	कारणात्मक अनुमान	स्वास्थ्य अर्थशास्त्र
परिवार	Regression model	Process / pipeline
उद्भव वर्ष	2000s–2010s	1990s (modern applications)
प्रवर्तक	McCandless, Gustafson & Austin (2007); Gustafson (2015)	Angrist & Pischke (applied econometrics); rooted in econometric theory
प्रकार	Bayesian causal sensitivity analysis	Method
मौलिक स्रोत	McCandless, L. C., Gustafson, P., & Austin, P. C. (2007). Bayesian propensity score analysis for observational data. Statistics in Medicine, 26(8), 1704-1718.	Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.
उपनाम	Bayesian sensitivity analysis, Bayesian bias analysis, probabilistic sensitivity analysis for confounding, Bayesian unmeasured confounding analysis	IV, two-stage least squares, TSLS, causal estimation
संबंधित	6	3
सारांश	Bayesian sensitivity analysis for causality quantifies how much an unmeasured confounder would need to influence both treatment assignment and outcome to overturn a causal conclusion. Rather than testing a single worst-case scenario, it places prior distributions over the strength of hidden confounding, propagates uncertainty through a full Bayesian model, and reports a posterior distribution for the causal effect that honestly reflects what is and is not identified from observed data.	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.