Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Identification causale avec les graphes acycliques dirigés (do-calculus)× | Analyse de sensibilité au biais caché (Bornes de Rosenbaum / E-value)× | |
|---|---|---|
| Domaine | Inférence causale | Inférence causale |
| Famille | Regression model | Regression model |
| Année d'origine≠ | 2009 | 2002 |
| Auteur d'origine≠ | Judea Pearl | Paul R. Rosenbaum (bounds); Tyler J. VanderWeele & Peng Ding (E-value) |
| Type≠ | Causal identification framework | Sensitivity analysis for causal inference |
| Source fondatrice≠ | 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 |
| Alias≠ | do-calculus, backdoor adjustment, Pearl causal identification, DAG ile Nedensel Tanımlama (do-calculus) | Rosenbaum bounds, E-value, hidden bias sensitivity analysis, unmeasured confounding sensitivity |
| Apparentées | 5 | 5 |
| Résumé≠ | 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). |
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