Salīdzināt metodes

	Beieziešu tendenču rādītāju svēršana	Apgrieztā varbūtības svēršana (IPW / IPTW)
Nozare	Cēloņsakarību secināšana	Cēloņsakarību secināšana
Saime	Regression model	Regression model
Izcelsmes gads	2009	2000
Autors	McCandless, Gustafson & Austin	Robins, Hernán & Brumback
Tips	Bayesian causal weighting estimator	Causal inference weighting estimator
Pirmavots	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.
Citi nosaukumi	Bayesian PSW, Bayesian IPW, Bayesian inverse probability weighting, Bayesian propensity weighting	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Saistītās	6	5
Kopsavilkums	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.