Módszerek összehasonlítása
Tekintse át a kiválasztott módszereket egymás mellett; az eltérő sorok kiemelve jelennek meg.
| Az inverz valószínűségi kezelési súlyozás (IPW / IPTW)× | A kauzális azonosítás irányított aciklikus grafikonokkal (do-kalkulus)× | |
|---|---|---|
| Tudományterület | Oksági következtetés | Oksági következtetés |
| Módszercsalád | Regression model | Regression model |
| Keletkezés éve≠ | 2000 | 2009 |
| Megalkotó≠ | Robins, Hernán & Brumback | Judea Pearl |
| Típus≠ | Causal inference weighting estimator | Causal identification framework |
| Alapmű≠ | Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560. DOI ↗ | Pearl, J. (2009). Causality: Models, Reasoning, and Inference (2nd ed.). Cambridge University Press. ISBN: 978-0521895606 |
| Alternatív nevek≠ | IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting | do-calculus, backdoor adjustment, Pearl causal identification, DAG ile Nedensel Tanımlama (do-calculus) |
| Kapcsolódó | 5 | 5 |
| Összefoglaló≠ | Inverse Probability Weighting is a causal-inference method that assigns each observation a weight equal to the inverse of its probability of receiving the treatment it actually received. Introduced by Robins, Hernán and Brumback (2000) for marginal structural models, it builds a pseudo-population in which treatment is independent of measured confounders, balancing selection bias. | 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. |
| ScholarGateAdatkészlet ↗ |
|
|