Linganisha mbinu

	Modeli tambuzi ya muundo wa pembeni ya Kibayesiani	Tofauti-katika-Tofauti za Kibayesia
Nyanja	Uhitimisho wa Kisababishi	Uhitimisho wa Kisababishi
Familia	Regression model	Regression model
Mwaka wa asili	2015 (Bayesian extension); 2000 (MSM foundation)	2015-2023
Mwanzilishi	Saarela, 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)
Aina	Causal inference / Bayesian weighted regression	Bayesian causal inference / panel regression
Chanzo asilia	Saarela, O., Stephens, D. A., Moodie, E. E. M., & Klein, M. B. (2015). On Bayesian estimation of marginal structural models. Biometrics, 71(2), 279-288.	Li, F., & Marchand, J. (2023). Bayesian inference for difference-in-differences. Econometrics Journal, 26(3), 509-529.
Majina mbadala	Bayesian MSM, Bayesian MSM-IPW, Bayesian weighted structural model, Bayesian causal MSM	Bayesian DiD, Bayes DiD, Bayesian diff-in-diff, Bayesian panel causal estimator
Zinazohusiana	6	5
Muhtasari	Bayesian 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.