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	DAG Causal Identification	छिपे हुए पूर्वाग्रह के लिए संवेदनशीलता विश्लेषण (रोजनबाम बाउंड्स / ई-वैल्यू)
क्षेत्र	कारणात्मक अनुमान	कारणात्मक अनुमान
परिवार	Regression model	Regression model
उद्भव वर्ष	2009	2002
प्रवर्तक	Judea Pearl	Paul R. Rosenbaum (bounds); Tyler J. VanderWeele & Peng Ding (E-value)
प्रकार	Causal identification framework	Sensitivity analysis for causal inference
मौलिक स्रोत	Pearl, J. (2009). Causality: Models, Reasoning, and Inference (2nd ed.). Cambridge University Press. ISBN: 978-0521895606	Rosenbaum, P. R. (2002). Observational Studies (2nd ed.). Springer. ISBN: 978-0387989679
उपनाम	do-calculus, backdoor adjustment, Pearl causal identification, DAG ile Nedensel Tanımlama (do-calculus)	Rosenbaum bounds, E-value, hidden bias sensitivity analysis, unmeasured confounding sensitivity
संबंधित	5	5
सारांश	DAG causal identification is a framework, developed by Judea Pearl (2009), that encodes causal assumptions as a directed acyclic graph and uses the do-calculus rules to determine whether and how a causal effect can be identified from observational data. It systematically handles confounders, instrumental variables, and backdoor paths.	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).