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	कारणता के लिए बायेसियन संवेदनशीलता विश्लेषण	कारणता के लिए संवेदनशीलता विश्लेषण
क्षेत्र	कारणात्मक अनुमान	कारणात्मक अनुमान
परिवार	Regression model	Regression model
उद्भव वर्ष	2000s–2010s	1983–2002
प्रवर्तक	McCandless, Gustafson & Austin (2007); Gustafson (2015)	Paul R. Rosenbaum (hidden-bias framework); extended by Cinelli & Hazlett (omitted-variable approach)
प्रकार	Bayesian causal sensitivity analysis	Diagnostic / robustness check
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.	Rosenbaum, P. R. (2002). Observational Studies (2nd ed.). Springer. ISBN: 978-0387989679
उपनाम	Bayesian sensitivity analysis, Bayesian bias analysis, probabilistic sensitivity analysis for confounding, Bayesian unmeasured confounding analysis	sensitivity analysis, hidden-bias sensitivity analysis, Rosenbaum sensitivity analysis, omitted-variable sensitivity
संबंधित	6	4
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.	Sensitivity analysis for causality assesses how robust a causal conclusion is to unobserved confounding. Rather than assuming all confounders are controlled, it asks: how strong would an unmeasured variable need to be to overturn the estimated effect? It is an indispensable robustness check after any quasi-experimental or observational causal analysis.