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	Ponderazione con punteggio di propensione multi-periodo	Inverse Probability of Treatment Weighting (IPW / IPTW)
Campo	Inferenza causale	Inferenza causale
Famiglia	Regression model	Regression model
Anno di origine	2000	2000
Ideatore	Robins, Hernán, and Brumback (building on Robins' g-computation framework)	Robins, Hernán & Brumback
Tipo	Quasi-experimental causal inference	Causal inference weighting estimator
Fonte seminale	Hernán, M. A., & Robins, J. M. (2020). Causal Inference: What If. Chapman & Hall/CRC.	Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.
Alias	longitudinal propensity score weighting, multi-wave PSW, time-varying propensity score weighting, sequential propensity score weighting	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Correlati	5	5
Sintesi	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.	Inverse Probability Weighting is a causal-inference method that assigns each observation a weight equal to the inverse of its probability of receiving the treatment it actually received. Introduced by Robins, Hernán and Brumback (2000) for marginal structural models, it builds a pseudo-population in which treatment is independent of measured confounders, balancing selection bias.