Sammenlign metoder

	Bayesiansk Dobbelt Robust Estimation	Vægtning med den inverse behandlingssandsynlighed (IPW / IPTW)
Fagområde	Kausal inferens	Kausal inferens
Familie	Regression model	Regression model
Oprindelsesår	2005–2010s	2000
Ophavsperson	Bang & Robins (2005); Bayesian extensions by Scharfstein, Kennedy, and others	Robins, Hernán & Brumback
Type	Semiparametric causal estimation with Bayesian inference	Causal inference weighting estimator
Oprindelig kilde	Bang, H., & Robins, J. M. (2005). Doubly robust estimation in missing data and causal inference models. Biometrics, 61(4), 962-973.	Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.
Aliasser	Bayesian DR, Bayesian AIPW, Bayesian augmented inverse probability weighting, Bayesian semiparametric causal estimation	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Relaterede	5	5
Resumé	Bayesian Doubly Robust Estimation combines the classical doubly robust (DR) augmented inverse probability weighting framework with Bayesian inference. It simultaneously models the propensity score and the outcome regression, placing prior distributions over both, and derives a posterior distribution over the average treatment effect that remains consistent even if one of the two component models is misspecified.	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.