ScholarGate
Assistent
Regression modelQuasi-experimental / causal inference

Bayesiansk Marginal Strukturel Model

Bayesiansk Marginal Strukturel Model (Bayesian MSM) kombinerer den kausale identificeringskraft fra invers-sandsynligheds-vægtede marginale strukturelle modeller med Bayesiansk posterior inferens. I stedet for at basere sig på punktestimater og asymptotiske standardfejl, propagerer den usikkerhed gennem en fuld posteriorfordeling over kausale effektparametre, hvilket tilbyder en kohærent kvantificering af usikkerhed for kausale effekter af tidsvarierende behandlinger.

Åbn i MethodMindSnartVideoSnartDownload slides

Læs hele metoden

Kun for medlemmer

Log ind med en gratis konto for at læse dette afsnit.

Log ind

Method map

The neighbourhood of related methods — select a node to explore.

Kilder

  1. Saarela, O., Stephens, D. A., Moodie, E. E. M., & Klein, M. B. (2015). On Bayesian estimation of marginal structural models. Biometrics, 71(2), 279-288. DOI: 10.1111/biom.12269
  2. Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560. DOI: 10.1097/00001648-200009000-00011

Sådan citerer du denne side

ScholarGate. (2026, June 3). Bayesian Marginal Structural Model with Inverse Probability Weighting. ScholarGate. https://scholargate.app/da/causal-inference/bayesian-marginal-structural-model

Which method?

Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.

Compare side by side
ScholarGateBayesian Marginal Structural Model (Bayesian Marginal Structural Model with Inverse Probability Weighting). Hentet 2026-06-15 fra https://scholargate.app/da/causal-inference/bayesian-marginal-structural-model · Datasæt: https://doi.org/10.5281/zenodo.20539026