Сравнение на методи

	Байесов анализ на чувствителността за причинно-следствена връзка	Метод на инструменталните променливи (IV) за причинно-следствен анализ
Област	Причинно-следствено заключение	Икономика на здравеопазването
Семейство	Regression model	Process / pipeline
Година на възникване	2000s–2010s	1990s (modern applications)
Създател	McCandless, Gustafson & Austin (2007); Gustafson (2015)	Angrist & Pischke (applied econometrics); rooted in econometric theory
Тип	Bayesian causal sensitivity analysis	Method
Основополагащ източник	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.
Други названия	Bayesian sensitivity analysis, Bayesian bias analysis, probabilistic sensitivity analysis for confounding, Bayesian unmeasured confounding analysis	IV, two-stage least squares, TSLS, causal estimation
Свързани	6	3
Резюме	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.