Methoden vergleichen

	Robust Inverse Probability Weighting (Robust IPW)	Propensity Score Weighting (PSW / IPW)
Fachgebiet	Kausale Inferenz	Kausale Inferenz
Familie	Regression model	Regression model
Entstehungsjahr	2000-2004	1983 (propensity score); 2003 (efficient IPW estimator)
Urheber	Lunceford & Davidian (2004); Robins, Hernán & Brumback (2000)	Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)
Typ	Causal weighting estimator	Causal inference / reweighting
Wegweisende Quelle	Lunceford, J. K., & Davidian, M. (2004). Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study. Statistics in Medicine, 23(19), 2937-2960.	Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41-55.
Aliasnamen	Robust IPW, Stabilized IPW, Trimmed IPW, Variance-robust IPW	PSW, inverse probability weighting, IPW, propensity-based weighting
Verwandt	5	6
Zusammenfassung	Robust Inverse Probability Weighting is a causal inference estimator that reweights observed units by stabilized or trimmed propensity score weights, then applies sandwich or bootstrap variance estimation to guard against model misspecification, extreme weights, and inflated standard errors. It extends standard IPW to improve finite-sample performance and inferential reliability in observational studies.	Propensity score weighting is a causal-inference method that reweights observations so that the covariate distributions of treated and untreated units look exchangeable, enabling unbiased estimation of average treatment effects from observational data. Each unit receives a weight that is the inverse of its probability of receiving the treatment it actually received — a strategy formalised by Rosenbaum and Rubin (1983) and given its efficient semiparametric form by Hirano, Imbens and Ridder (2003).