Linganisha mbinu

	Uzito wa Uwezekano Geu Kinabadilika	Uzito wa Alama ya Mwelekeo (PSW / IPW)
Nyanja	Uhitimisho wa Kisababishi	Uhitimisho wa Kisababishi
Familia	Regression model	Regression model
Mwaka wa asili	1986-2000	1983 (propensity score); 2003 (efficient IPW estimator)
Mwanzilishi	James M. Robins and colleagues	Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)
Aina	Causal weighting estimator	Causal inference / reweighting
Chanzo asilia	Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.	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.
Majina mbadala	Dynamic IPW, Time-varying IPW, Longitudinal IPW, Sequential IPW	PSW, inverse probability weighting, IPW, propensity-based weighting
Zinazohusiana	4	6
Muhtasari	Dynamic Inverse Probability Weighting (Dynamic IPW) estimates the causal effect of a time-varying treatment sequence by reweighting observed data to mimic a hypothetical randomised trial. Developed by Robins and colleagues in the context of marginal structural models, it handles the challenge that in longitudinal settings, past treatment affects future covariates, which in turn affect future treatment — a feedback loop that standard regression cannot untangle.	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).