Regression modelQuasi-experimental / causal inference

Bayesovska metoda sintetičke kontrole

Bayesovska metoda sintetičke kontrole procjenjuje uzročni učinak intervencije na jednu tretiranu jedinicu konstruiranjem probabilističkog protu-činjeničnog stanja iz ponderirane kombinacije netretiranih donatorskih jedinica. Za razliku od klasične SCM, ona postavlja apriorno razdiobu na sintetičke težine, dajući potpune posteriorne intervale nesigurnosti za protu-činjeničnu putanju i učinak tretmana u svakoj vremenskoj točki nakon intervencije.

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Izvori

  1. Brodersen, K. H., Gallusser, F., Koehler, J., Remy, N., & Scott, S. L. (2015). Inferring causal impact using Bayesian structural time-series models. Annals of Applied Statistics, 9(1), 247-274. DOI: 10.1214/14-AOAS788
  2. Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic control methods for comparative case studies: Estimating the effect of California's tobacco control program. Journal of the American Statistical Association, 105(490), 493-505. DOI: 10.1198/jasa.2009.ap08746

Kako citirati ovu stranicu

ScholarGate. (2026, June 3). Bayesian Synthetic Control Method. ScholarGate. https://scholargate.app/hr/causal-inference/bayesian-synthetic-control-method

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Citirana u

ScholarGateBayesian Synthetic Control Method (Bayesian Synthetic Control Method). Preuzeto 2026-06-15 s https://scholargate.app/hr/causal-inference/bayesian-synthetic-control-method · Skup podataka: https://doi.org/10.5281/zenodo.20539026