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مدل ساختاری حاشیه‌ای بیزی×تفاوت‌های بیزی در تفاوت‌ها (Bayesian Difference-in-Differences)×
حوزهاستنتاج علّیاستنتاج علّی
خانوادهRegression modelRegression model
سال پیدایش2015 (Bayesian extension); 2000 (MSM foundation)2015-2023
پدیدآورSaarela, Stephens, Moodie & Klein (Bayesian extension); Robins, Hernan & Brumback (original MSM)Li & Marchand (formal Bayesian DiD framework); Brodersen et al. (Bayesian causal inference in time series)
نوعCausal inference / Bayesian weighted regressionBayesian causal inference / panel regression
منبع بنیادینSaarela, 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 ↗Li, F., & Marchand, J. (2023). Bayesian inference for difference-in-differences. Econometrics Journal, 26(3), 509-529. link ↗
نام‌های دیگرBayesian MSM, Bayesian MSM-IPW, Bayesian weighted structural model, Bayesian causal MSMBayesian DiD, Bayes DiD, Bayesian diff-in-diff, Bayesian panel causal estimator
مرتبط65
خلاصهBayesian Marginal Structural Model (Bayesian MSM) combines the causal identification power of inverse-probability-weighted marginal structural models with Bayesian posterior inference. Rather than relying on point estimates and asymptotic standard errors, it propagates uncertainty through a full posterior distribution over causal effect parameters, offering coherent uncertainty quantification for causal effects of time-varying treatments.Bayesian Difference-in-Differences applies Bayesian statistical inference to the classic DiD design, replacing frequentist point estimates with full posterior distributions over the treatment effect. This yields not only an estimate of the causal effect but also a coherent probability statement about its magnitude and uncertainty, making it especially useful when sample sizes are modest or informative prior knowledge is available.
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ScholarGateمقایسهٔ روش‌ها: Bayesian Marginal Structural Model · Bayesian Difference-in-Differences. بازیابی‌شده در 2026-06-15 از https://scholargate.app/fa/compare