Bandingkan metode
Tinjau metode pilihan Anda berdampingan; baris yang berbeda akan disorot.
| Identifikasi Kausal dengan Graf Berarah Asiklik (do-calculus)× | Analisis Sensitivitas untuk Bias Tersembunyi (Batas Rosenbaum / Nilai-E)× | |
|---|---|---|
| Bidang | Inferensi Kausal | Inferensi Kausal |
| Keluarga | Regression model | Regression model |
| Tahun asal≠ | 2009 | 2002 |
| Pencetus≠ | Judea Pearl | Paul R. Rosenbaum (bounds); Tyler J. VanderWeele & Peng Ding (E-value) |
| Tipe≠ | Causal identification framework | Sensitivity analysis for causal inference |
| Sumber perintis≠ | 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 |
| Terkait | 5 | 5 |
| Ringkasan≠ | 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). |
| ScholarGateSet data ↗ |
|
|