Methoden vergleichen

	Robuste Gewichtung mittels Propensity Scores	Propensity Score Weighting (PSW / IPW)
Fachgebiet	Kausale Inferenz	Kausale Inferenz
Familie	Regression model	Regression model
Entstehungsjahr	1994–2019	1983 (propensity score); 2003 (efficient IPW estimator)
Urheber	Robins, Rotnitzky, & Zhao (foundational augmented IPW); Zhao, Small, & Bhattacharya (sensitivity-robust IPW)	Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)
Typ	Robust causal weighting estimator	Causal inference / reweighting
Wegweisende Quelle	Robins, J. M., Rotnitzky, A., & Zhao, L. P. (1994). Estimation of regression coefficients when some regressors are not always observed. Journal of the American Statistical Association, 89(427), 846-866.	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 PSW, robust IPW, robustness-augmented propensity score weighting, misspecification-robust weighting	PSW, inverse probability weighting, IPW, propensity-based weighting
Verwandt	6	6
Zusammenfassung	Robust Propensity Score Weighting extends standard inverse probability weighting by incorporating safeguards against misspecification of the propensity score model and extreme weights. It combines techniques such as weight trimming, overlap weighting, or augmented outcome models to ensure that causal effect estimates remain reliable even when the propensity score model is imperfectly specified.	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).