Сравнение методов

	Байесовское взвешивание по обратной вероятности	Взвешивание по обратной вероятности лечения (IPW / IPTW)
Область	Причинно-следственный вывод	Причинно-следственный вывод
Семейство	Regression model	Regression model
Год появления	2015	2000
Автор метода	Saarela, Stephens, Moodie & Klein (2015); Liao & Zigler (2020)	Robins, Hernán & Brumback
Тип	Bayesian causal weighting estimator	Causal inference weighting estimator
Основополагающий источник	Saarela, O., Stephens, D. A., Moodie, E. E. M., & Klein, M. B. (2015). On risk prediction and characterisation of treatment effects in a Bayesian framework using the propensity score. Statistics in Medicine, 34(14), 2170-2185.	Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.
Другие названия	Bayesian IPW, BIPW, Bayesian propensity-weighted estimation, Bayesian marginal structural weighting	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Связанные	6	5
Сводка	Bayesian Inverse Probability Weighting (Bayesian IPW) extends the classical IPW estimator by placing prior distributions over the propensity-score model parameters and propagating that uncertainty into the causal-effect estimate. The result is a posterior distribution for the average treatment effect that fully accounts for both propensity-score estimation uncertainty and outcome-model uncertainty, enabling credible-interval inference rather than relying on asymptotic approximations.	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.