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Bajezijanska analiza osetljivosti za kauzalnost

Bajezijanska analiza osetljivosti za kauzalnost kvantifikuje u kojoj meri bi nemereni konfounder morao da utiče i na dodelu tretmana i na ishod da bi se poništio kauzalni zaključak. Umesto testiranja jednog scenarija najgoreg slučaja, ona postavlja apriorne distribucije za jačinu skrivenog konfoundinga, propagira nesigurnost kroz potpuni bajezijanski model i izveštava o aposteriornoj distribuciji za kauzalni efekat koja iskreno odražava ono što je identifikovano i ono što nije identifikovano iz posmatranih podataka.

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Izvori

  1. McCandless, L. C., Gustafson, P., & Austin, P. C. (2007). Bayesian propensity score analysis for observational data. Statistics in Medicine, 26(8), 1704-1718. DOI: 10.1002/sim.3460
  2. Gustafson, P. (2015). Bayesian Inference for Partially Identified Models: Exploring the Limits of Limited Data. CRC Press / Chapman & Hall. ISBN: 9781439869390

Kako citirati ovu stranicu

ScholarGate. (2026, June 3). Bayesian Sensitivity Analysis for Unmeasured Confounding in Causal Inference. ScholarGate. https://scholargate.app/sr/causal-inference/bayesian-sensitivity-analysis-for-causality

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ScholarGateBayesian Sensitivity Analysis for Causality (Bayesian Sensitivity Analysis for Unmeasured Confounding in Causal Inference). Preuzeto 2026-06-15 sa https://scholargate.app/sr/causal-inference/bayesian-sensitivity-analysis-for-causality · Skup podataka: https://doi.org/10.5281/zenodo.20539026