Methoden vergleichen

	Robuste Gewichtung mittels Propensity Scores	Propensity Score Matching
Fachgebiet	Kausale Inferenz	Forschungsstatistik
Familie	Regression model	Process / pipeline
Entstehungsjahr	1994–2019	1983
Urheber	Robins, Rotnitzky, & Zhao (foundational augmented IPW); Zhao, Small, & Bhattacharya (sensitivity-robust IPW)	Paul Rosenbaum and Donald Rubin
Typ	Robust causal weighting estimator	Method
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	PSM, propensity score weighting, covariate balance
Verwandt	6	3
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 matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias.