Linganisha mbinu

	Uchambuzi wa hisia kwa upendeleo uliofichwa (Vipimo vya Rosenbaum / E-value)	Urekebishaji wa mlango-mbele (Kigezo cha mlango-mbele)
Nyanja	Uhitimisho wa Kisababishi	Uhitimisho wa Kisababishi
Familia	Regression model	Regression model
Mwaka wa asili	2002	1995
Mwanzilishi	Paul R. Rosenbaum (bounds); Tyler J. VanderWeele & Peng Ding (E-value)	Judea Pearl
Aina	Sensitivity analysis for causal inference	Causal identification (graphical adjustment)
Chanzo asilia	Rosenbaum, P. R. (2002). Observational Studies (2nd ed.). Springer. ISBN: 978-0387989679	Pearl, J. (1995). Causal Diagrams for Empirical Research. Biometrika, 82(4), 669-688.
Majina mbadala	Rosenbaum bounds, E-value, hidden bias sensitivity analysis, unmeasured confounding sensitivity	frontdoor criterion, Pearl's frontdoor adjustment, frontdoor formula, Ön Kapı Düzenlemesi (Frontdoor Adjustment)
Zinazohusiana	5	4
Muhtasari	Sensitivity analysis for hidden bias is a family of methods that quantify how strongly an unmeasured confounder would have to operate before it could overturn a causal conclusion drawn from observational data. It was crystallised by Paul Rosenbaum's sensitivity bounds (2002) and extended by VanderWeele and Ding's E-value (2017).	Frontdoor adjustment is Judea Pearl's graphical identification strategy, introduced in 1995, that recovers the causal effect of a treatment on an outcome through a fully mediating variable even when an unobserved confounder sits between the treatment and the outcome. It is the go-to tool when the backdoor criterion cannot be satisfied because the confounder is unmeasured.