Linganisha mbinu

	Uzani wa Alama ya Kuendesha kwa Mfumo wa Bayesian	Uzito wa Kinyume wa Uwezekano wa Matibabu (IPW / IPTW)
Nyanja	Uhitimisho wa Kisababishi	Uhitimisho wa Kisababishi
Familia	Regression model	Regression model
Mwaka wa asili	2009	2000
Mwanzilishi	McCandless, Gustafson & Austin	Robins, Hernán & Brumback
Aina	Bayesian causal weighting estimator	Causal inference weighting estimator
Chanzo asilia	McCandless, L. C., Gustafson, P., & Austin, P. C. (2009). Bayesian propensity score analysis for observational data. Statistics in Medicine, 28(1), 94–112.	Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.
Majina mbadala	Bayesian PSW, Bayesian IPW, Bayesian inverse probability weighting, Bayesian propensity weighting	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Zinazohusiana	6	5
Muhtasari	Bayesian Propensity Score Weighting estimates causal treatment effects in observational data by combining a Bayesian model for the propensity score with inverse probability weighting. By placing a prior over propensity-score parameters and propagating posterior uncertainty through the weighting step, this approach yields fully probabilistic uncertainty intervals for the average treatment effect, accounting for the uncertainty in both the score model and the outcome.	Inverse Probability Weighting is a causal-inference method that assigns each observation a weight equal to the inverse of its probability of receiving the treatment it actually received. Introduced by Robins, Hernán and Brumback (2000) for marginal structural models, it builds a pseudo-population in which treatment is independent of measured confounders, balancing selection bias.