Comparison of Propensity Score Weighting Methods to Remove Selection Bias in Average Treatment Effect Estimates
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Tarih
2023
Yazarlar
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Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
In this Monte Carlo simulation study, the performance of six different propensity score methods implemented through weighting cases was investigated: inverse probability of treatment weighting, truncated inverse probability of treatment weighting, propensity score stratification, marginal mean weighting through propensity score stratification, optimal full propensity score matching, and marginal mean weighting through optimal full propensity score matching. These methods aim to reduce selection bias in estimates of the average treatment effect (ATE) in observational studies. For the estimation of standard errors of the ATE with weights, three methods were compared: weighted least squares (WLS), Taylor series linearization (TSL), and jackknife (JK). Results indicated that covariance adjustment extensions of the investigated propensity score methods, in combination with TSL and JK standard error estimation methods, remove the selection bias appropriately and provide the most accurate standard errors under the simulated conditions.
Açıklama
Anahtar Kelimeler
Observational Study, Propensity Score Methods, Average Treatment Effect, Standard Error
Kaynak
Uluslararası Türk Eğitim Bilimleri Dergisi
WoS Q Değeri
Scopus Q Değeri
Cilt
2023
Sayı
21
