Methoden vergelijken

	Propensity Score Weighting (PSW / IPW)	Propensity Score Matching
Vakgebied	Causale inferentie	Onderzoeksstatistiek
Familie	Regression model	Process / pipeline
Jaar van ontstaan	1983 (propensity score); 2003 (efficient IPW estimator)	1983
Grondlegger	Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)	Paul Rosenbaum and Donald Rubin
Type	Causal inference / reweighting	Method
Oorspronkelijke bron	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.	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.
Aliassen	PSW, inverse probability weighting, IPW, propensity-based weighting	PSM, propensity score weighting, covariate balance
Verwant	6	3
Samenvatting	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).	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.