Methoden vergelijken

	Multi-period Propensity Score Weighting	Propensity Score Weighting (PSW / IPW)
Vakgebied	Causale inferentie	Causale inferentie
Familie	Regression model	Regression model
Jaar van ontstaan	2000	1983 (propensity score); 2003 (efficient IPW estimator)
Grondlegger	Robins, Hernán, and Brumback (building on Robins' g-computation framework)	Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)
Type	Quasi-experimental causal inference	Causal inference / reweighting
Oorspronkelijke bron	Hernán, M. A., & Robins, J. M. (2020). Causal Inference: What If. Chapman & Hall/CRC.	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	longitudinal propensity score weighting, multi-wave PSW, time-varying propensity score weighting, sequential propensity score weighting	PSW, inverse probability weighting, IPW, propensity-based weighting
Verwant	5	6
Samenvatting	Multi-period propensity score weighting extends the standard propensity score weighting framework to settings with repeated measurements and time-varying treatments. It constructs stabilised inverse probability weights (IPW) at each time point so that the weighted sample resembles a sequence of randomised experiments, allowing unbiased estimation of causal effects under longitudinal confounding.	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).