Methoden vergleichen

	Bayesian Propensity Score Matching	Inverse Probability of Treatment Weighting (IPW / IPTW)
Fachgebiet	Kausale Inferenz	Kausale Inferenz
Familie	Regression model	Regression model
Entstehungsjahr	2012	2000
Urheber	Kaplan & Chen (2012); foundational PSM by Rosenbaum & Rubin (1983)	Robins, Hernán & Brumback
Typ	Bayesian causal inference / matching	Causal inference weighting estimator
Wegweisende Quelle	Kaplan, D., & Chen, J. (2012). A Two-Step Bayesian Approach for Propensity Score Analysis: Simulations and Case Study. Psychometrika, 77(3), 581-609.	Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.
Aliasnamen	Bayesian PSM, BPSM, Bayesian matching estimator, Bayesian propensity weighting	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Verwandt	6	5
Zusammenfassung	Bayesian Propensity Score Matching (Bayesian PSM) extends classical propensity score matching by placing a prior distribution over the propensity model parameters and propagating posterior uncertainty through the matching and outcome stages. Introduced formally by Kaplan and Chen (2012), it offers a principled account of estimation uncertainty that frequentist matching commonly ignores, and allows incorporation of substantive prior knowledge about treatment selection.	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.