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Regression modelQuasi-experimental / causal inference

Robust Propensity Score Weighting

Robust Propensity Score Weighting utvider standard invers sannsynlighetsvekting ved å inkludere beskyttelsestiltak mot feilspesifisering av propensity score-modellen og ekstreme vekter. Den kombinerer teknikker som vekttrimming, overlappvekting eller utvidede utfallmodeller for å sikre at estimater av kausale effekter forblir pålitelige selv når propensity score-modellen er ufullstendig spesifisert.

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  1. Robins, J. M., Rotnitzky, A., & Zhao, L. P. (1994). Estimation of regression coefficients when some regressors are not always observed. Journal of the American Statistical Association, 89(427), 846-866. DOI: 10.1080/01621459.1994.10476818
  2. Zhao, Q., Small, D. S., & Bhattacharya, B. B. (2019). Sensitivity analysis for inverse probability weighting estimators via the percentile bootstrap. Journal of the Royal Statistical Society: Series B, 81(4), 735-761. DOI: 10.1111/rssb.12327

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ScholarGate. (2026, June 3). Robust Propensity Score Weighting Estimator. ScholarGate. https://scholargate.app/no/causal-inference/robust-propensity-score-weighting

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ScholarGateRobust Propensity Score Weighting (Robust Propensity Score Weighting Estimator). Hentet 2026-06-15 fra https://scholargate.app/no/causal-inference/robust-propensity-score-weighting · Datasett: https://doi.org/10.5281/zenodo.20539026