방법 비교

	베이지안 성향 점수 가중법	역확률 가중치 (Inverse Probability Weighting, IPW / IPTW)
분야	인과추론	인과추론
계열	Regression model	Regression model
기원 연도	2009	2000
창시자	McCandless, Gustafson & Austin	Robins, Hernán & Brumback
유형	Bayesian causal weighting estimator	Causal inference weighting estimator
원전	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.
별칭	Bayesian PSW, Bayesian IPW, Bayesian inverse probability weighting, Bayesian propensity weighting	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
관련	6	5
요약	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.