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	Ponderazione con punteggio di propensione multi-periodo	Modello Strutturale Marginale (MSM)
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)	James M. Robins, Miguel A. Hernan, Babette Brumback
Tipo	Quasi-experimental causal inference	Causal model / semiparametric weighting
Fonte seminale	Hernán, M. A., & Robins, J. M. (2020). Causal Inference: What If. Chapman & Hall/CRC.	Robins, J. M., Hernan, 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	MSM, MSM-IPTW, marginal structural Cox model, weighted structural model
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.	A marginal structural model is a causal modeling framework designed to estimate the effect of a time-varying treatment in the presence of time-varying confounders that are themselves affected by prior treatment. By reweighting observations with inverse probability of treatment weights, MSMs create a pseudo-population in which confounding is eliminated, enabling unbiased estimation of causal treatment contrasts even when standard regression adjustments would fail.