Linganisha mbinu

	Panel Data Inverse Probability Weighting	Uzito wa Alama ya Mwelekeo (PSW / IPW)
Nyanja	Uhitimisho wa Kisababishi	Uhitimisho wa Kisababishi
Familia	Regression model	Regression model
Mwaka wa asili	2000	1983 (propensity score); 2003 (efficient IPW estimator)
Mwanzilishi	Robins, Hernan & Brumback	Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)
Aina	Reweighting / causal inference	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	panel IPW, longitudinal IPW, time-varying IPW, panel IPTW	PSW, inverse probability weighting, IPW, propensity-based weighting
Zinazohusiana	5	6
Muhtasari	Panel Data Inverse Probability Weighting (panel IPW) estimates the causal effect of a time-varying treatment by reweighting observed units to create a pseudo-population in which treatment is independent of measured confounders at each time point. It extends the cross-sectional IPW framework to longitudinal settings where treatment status and confounders both evolve across multiple periods.	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).