Compara mètodes

	Ponderació robusta per puntuació de propensió	Emparellament per puntuació de propensió
Camp	Inferència causal	Estadística per a la recerca
Família	Regression model	Process / pipeline
Any d'origen	1994–2019	1983
Autor original	Robins, Rotnitzky, & Zhao (foundational augmented IPW); Zhao, Small, & Bhattacharya (sensitivity-robust IPW)	Paul Rosenbaum and Donald Rubin
Tipus	Robust causal weighting estimator	Method
Font seminal	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.
Àlies	robust PSW, robust IPW, robustness-augmented propensity score weighting, misspecification-robust weighting	PSM, propensity score weighting, covariate balance
Relacionats	6	3
Resum	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.