Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Ponderación por Probabilidad Inversa de Tratamiento (IPW / IPTW)× | La identificación causal con grafos acíclicos dirigidos (cálculo-do)× | |
|---|---|---|
| Campo | Inferencia causal | Inferencia causal |
| Familia | Regression model | Regression model |
| Año de origen≠ | 2000 | 2009 |
| Autor original≠ | Robins, Hernán & Brumback | Judea Pearl |
| Tipo≠ | Causal inference weighting estimator | Causal identification framework |
| Fuente seminal≠ | 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 |
| Alias≠ | IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting | do-calculus, backdoor adjustment, Pearl causal identification, DAG ile Nedensel Tanımlama (do-calculus) |
| Relacionados | 5 | 5 |
| Resumen≠ | 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. |
| ScholarGateConjunto de datos ↗ |
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