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| DAG (Directed Acyclic Graph) による因果推論特定 (do-calculus)× | 隠れたバイアスに対する感度分析（ローゼンバウム限界値 / E値）× | |
|---|---|---|
| 分野 | 因果推論 | 因果推論 |
| 系統 | Regression model | Regression model |
| 提唱年≠ | 2009 | 2002 |
| 提唱者≠ | Judea Pearl | Paul R. Rosenbaum (bounds); Tyler J. VanderWeele & Peng Ding (E-value) |
| 種類≠ | Causal identification framework | Sensitivity analysis for causal inference |
| 原典≠ | 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 |
| 別名≠ | do-calculus, backdoor adjustment, Pearl causal identification, DAG ile Nedensel Tanımlama (do-calculus) | Rosenbaum bounds, E-value, hidden bias sensitivity analysis, unmeasured confounding sensitivity |
| 関連 | 5 | 5 |
| 概要≠ | 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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