Confronta i metodi

	Stima Bayesian Doubly Robust	Inverse Probability of Treatment Weighting (IPW / IPTW)
Campo	Inferenza causale	Inferenza causale
Famiglia	Regression model	Regression model
Anno di origine	2005–2010s	2000
Ideatore	Bang & Robins (2005); Bayesian extensions by Scharfstein, Kennedy, and others	Robins, Hernán & Brumback
Tipo	Semiparametric causal estimation with Bayesian inference	Causal inference weighting estimator
Fonte seminale	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.
Alias	Bayesian DR, Bayesian AIPW, Bayesian augmented inverse probability weighting, Bayesian semiparametric causal estimation	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Correlati	5	5
Sintesi	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.